References list

Sort by reference title
Sort by reference year
Sort by reference first author
Sort by reference journal


References

  • Pedroche F., Romance M., Criado R., 2016. A biplex approach to PageRank centrality: From classic to multiplex networks. Chaos, 26(6).DOI: 10.1063/1.4952955
  • Fu L., Gao L., Ma X., 2010. A centrality measure based on spectral optimization of modularity density. Science in China, Series F: Information Sciences, 53(9), pp.1727-1737.DOI: 10.1007/s11432-010-4043-4
  • Wang, Y., Chen, G. 2013, A centrality measure based on two layer neighbors for complex networks. 9: 1 (2013) 25–32.
  • Kumar R., Manuel S. (2019) A Centrality Measure for Directed Networks: m-Ranking Method. In: Özyer T., Bakshi S., Alhajj R. (eds) Social Networks and Surveillance for Society. Lecture Notes in Social Networks. Springer, Cham.DOI: 10.1007/978-3-319-78256-0_7
  • Agryzkov T., Tortosa L., Vicent J.F., Wilson R., 2019. A centrality measure for urban networks based on the eigenvector centrality concept. Environment and Planning B: Urban Analytics and City Science, 46(4), pp.668-689.DOI: 10.1177/2399808317724444
  • Meghanathan, N., 2017. A computationally lightweight and localized centrality metric in lieu of betweenness centrality for complex network analysis. Vietnam Journal of Computer Science, 4(1), pp.23-38.DOI: 10.1007/s40595-016-0073-1
  • Wang Q., Yu X., Zhang X., 2013. A connectionist model-based approach to centrality discovery in social networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8178 LNAI, pp.82-94.DOI: 10.1007/978-3-319-04048-6_8
  • Gurfinkel, A.J. and Rikvold, P.A., 2020. A Current-Flow Centrality With Adjustable Reach. arXiv preprint arXiv:2005.14356.
  • Guo L., Zhang W.Y., Luo Z.J., Gao F.J., Zhang Y.C., 2017. A dynamical approach to identify vertices′ centrality in complex networks. Physics Letters, Section A: General, Atomic and Solid State Physics, 381(48), pp.3972-3977.DOI: 10.1016/j.physleta.2017.10.033
  • Riveros C., Salas J., 2020. A family of centrality measures for graph data based on subgraphs. Leibniz International Proceedings in Informatics, LIPIcs, 155.DOI: 10.4230/LIPIcs.ICDT.2020.23
  • Davidsen S., Padmavathamma M., 2014. A fuzzy closeness centrality using andness-direction to control degree of closeness. 1st International Conference on Networks and Soft Computing, ICNSC 2014 - Proceedings, , pp.203-208.DOI: 10.1109/CNSC.2014.6906711
  • Deng H., Lyu M., King I., 2009. A generalized Co-HITS algorithm and its application to bipartite graphs. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, , pp.239-247.DOI: 10.1145/1557019.1557051
  • Jacobsen K., Tien J., 2018. A generalized inverse for graphs with absorption. Linear Algebra and Its Applications, 537, pp.118-147.DOI: 10.1016/j.laa.2017.09.029
  • Borgatti S., Everett M., 2006. A Graph-theoretic perspective on centrality. Social Networks, 28(4), pp.466-484.DOI: 10.1016/j.socnet.2005.11.005
  • Stelzl U., Worm U., Lalowski M., Haenig C., Brembeck F.H., Goehler H., Stroedicke M., Zenkner M., Schoenherr A., Koeppen S., Timm J., Mintzlaff S., Abraham C., Bock N., Kietzmann S., Goedde A., Toksöz E., Droege A., Krobitsch S., Korn B., Birchmeier W., Lehrach H., Wanker E.E., 2005. A human protein-protein interaction network: A resource for annotating the proteome. Cell, 122(6), pp.957-968.DOI: 10.1016/j.cell.2005.08.029
  • Kanwar K., Kaushal S., Kumar H., 2019. A hybrid node ranking technique for finding influential nodes in complex social networks. Library Hi Tech, .DOI: 10.1108/LHT-01-2019-0019
  • Ilyas M., Radha H., 2010. A KLT-inspired node centrality for identifying influential neighborhoods in graphs. 2010 44th Annual Conference on Information Sciences and Systems, CISS 2010, .DOI: 10.1109/CISS.2010.5464971
  • Li M., Wang J., Chen X., Wang H., Pan Y., 2011. A local average connectivity-based method for identifying essential proteins from the network level. Computational Biology and Chemistry, 35(3), pp.143-150.DOI: 10.1016/j.compbiolchem.2011.04.002
  • Lu P., Yu J.J., 2020. A mixed clustering coefficient centrality for identifying essential proteins. International Journal of Modern Physics B, 34(10).DOI: 10.1142/S0217979220500903
  • Wang Y., Wang S., Deng Y., 2019. A modified efficiency centrality to identify influential nodes in weighted networks. Pramana - Journal of Physics, 92(4).DOI: 10.1007/s12043-019-1727-1
  • Mazalov V.V., Khitraya V.A., 2021. A Modified Myerson Value for Determining the Centrality of Graph Vertices. Automation and Remote Control, 82(1), pp.145-159.DOI: 10.1134/S0005117921010100
  • Zhang B., Zhang L., Mu C., Zhao Q., Song Q., Hong X., 2019. A most influential node group discovery method for influence maximization in social networks: A trust-based perspective. Data and Knowledge Engineering, 121, pp.71-87.DOI: 10.1016/j.datak.2019.05.001
  • Vega-Oliveros D.A., Gomes P.S., E. Milios E., Berton L., 2019. A multi-centrality index for graph-based keyword extraction. Information Processing and Management, 56(6).DOI: 10.1016/j.ipm.2019.102063
  • Agryzkov T., Oliver J., Tortosa L., Vicent J., 2014. A new betweenness centrality measure based on an algorithm for ranking the nodes of a network. Applied Mathematics and Computation, 244, pp.467-478.DOI: 10.1016/j.amc.2014.07.026
  • Lv L., Zhang K., Bardou D., Zhang T., Zhang J., Cai Y., Jiang T., 2019. A new centrality measure based on random walks for multilayer networks under the framework of tensor computation. Physica A: Statistical Mechanics and its Applications, 526.DOI: 10.1016/j.physa.2019.04.236
  • Berahmand K., Bouyer A., Samadi N., 2018. A new centrality measure based on the negative and positive effects of clustering coefficient for identifying influential spreaders in complex networks. Chaos, Solitons and Fractals, 110, pp.41-54.DOI: 10.1016/j.chaos.2018.03.014
  • Lv L., Zhang K., Bardou D., Zhang T., Cai Y., 2019. A new centrality measure based on topologically biased random walks for multilayer networks. Journal of the Physical Society of Japan, 88(2).DOI: 10.7566/JPSJ.88.024010
  • Kundu S., Murthy C., Pal S., 2011. A new centrality measure for influence maximization in social networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6744 LNCS, pp.242-247.DOI: 10.1007/978-3-642-21786-9_40
  • Ide, K., Namatame, A., Ponnambalam, L., Xiuju, F. and Goh, R.S.M., 2014. A new centrality measure for probabilistic diffusion in network. Advances in Computer Science: An International Journal, 3(5), pp.115-121.
  • Wang D., Zou X., 2018. A new centrality measure of nodes in multilayer networks under the framework of tensor computation. Applied Mathematical Modelling, 54, pp.46-63.DOI: 10.1016/j.apm.2017.07.012
  • Du, Y., Gao, C., Chen, X., Hu, Y., Sadiq, R. and Deng, Y., 2015. A new closeness centrality measure via effective distance in complex networks. Chaos: An Interdisciplinary Journal of Nonlinear Science, 25(3), p.033112.DOI: 10.1063/1.4916215
  • Li M., Zhang H., Wang J., Pan Y., 2012. A new essential protein discovery method based on the integration of protein-protein interaction and gene expression data. BMC Systems Biology, 6.DOI: 10.1186/1752-0509-6-15
  • Fushimi T., Saito K., Ikeda T., Kazama K., 2019. A new group centrality measure for maximizing the connectedness of network under uncertain connectivity. Studies in Computational Intelligence, 812, pp.3-14.DOI: 10.1007/978-3-030-05411-3_1
  • Joyce K., Laurienti P., Burdette J., Hayasaka S., 2010. A new measure of centrality for brain networks. PLoS ONE, 5(8).DOI: 10.1371/journal.pone.0012200
  • Wang S., Du Y., Deng Y., 2017. A new measure of identifying influential nodes: Efficiency centrality. Communications in Nonlinear Science and Numerical Simulation, 47, pp.151-163.DOI: 10.1016/j.cnsns.2016.11.008
  • Jianwei W., Lili R., Tianzhu G., 2008. A new measure of node importance in complex networks with tunable parameters. 2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008, .DOI: 10.1109/WiCom.2008.1170
  • Wang H., Li M., Wang J., Pan Y., 2011. A new method for identifying essential proteins based on edge clustering coefficient. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6674 LNBI, pp.87-98.DOI: 10.1007/978-3-642-21260-4_12
  • Zhang X., Xu J., Xiao W.x., 2013. A New Method for the Discovery of Essential Proteins. PLoS ONE, 8(3).DOI: 10.1371/journal.pone.0058763
  • Fei L., Mo H., Deng Y., 2017. A new method to identify influential nodes based on combining of existing centrality measures. Modern Physics Letters B, 31(26).DOI: 10.1142/S0217984917502438
  • Wang Z., Dueñas-Osorio L., Padgett J., 2015. A new mutually reinforcing network node and link ranking algorithm. Scientific Reports, 5.DOI: 10.1038/srep15141
  • Katz L., 1953. A new status index derived from sociometric analysis. Psychometrika, 18(1), pp.39-43.DOI: 10.1007/BF02289026
  • Qi X., Fuller E., Luo R., Zhang C.Q., 2015. A novel centrality method for weighted networks based on the Kirchhoff polynomial. Pattern Recognition Letters, 58, pp.51-60.DOI: 10.1016/j.patrec.2015.02.007
  • Coutinho R., Boukerche A., Vieira L., Loureiro A., 2016. A novel centrality metric for topology control in underwater sensor networks. MSWiM 2016 - Proceedings of the 19th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, , pp.205-212.DOI: 10.1145/2988287.2989162
  • Qiao T., Shan W., Yu G., Liu C., 2018. A novel entropy-based centrality approach for identifying vital nodes in weighted networks. Entropy, 20(4).DOI: 10.3390/e20040261
  • Clemente G.P., Cornaro A., 2020. A novel measure of edge and vertex centrality for assessing robustness in complex networks. Soft Computing, 24(18), pp.13687-13704.DOI: 10.1007/s00500-019-04470-w
  • De Meo P., Ferrara E., Fiumara G., Ricciardello A., 2012. A novel measure of edge centrality in social networks. Knowledge-Based Systems, 30, pp.136-150.DOI: 10.1016/j.knosys.2012.01.007
  • Zhao J., Song Y., Deng Y., 2020. A novel model to identify the influential nodes: Evidence theory centrality. IEEE Access, 8, pp.46773-46780.DOI: 10.1109/ACCESS.2020.2978142
  • Karabekmez M., Kirdar B., 2016. A novel topological centrality measure capturing biologically important proteins. Molecular BioSystems, 12(2), pp.666-673.DOI: 10.1039/C5MB00732A
  • Wang J., Hou X., Li K., Ding Y., 2017. A novel weight neighborhood centrality algorithm for identifying influential spreaders in complex networks. Physica A: Statistical Mechanics and its Applications, 475, pp.88-105.DOI: 10.1016/j.physa.2017.02.007
  • Kwon H., Choi Y.H., Lee J.M., 2019. A Physarum Centrality Measure of the Human Brain Network. Scientific Reports, 9(1).DOI: 10.1038/s41598-019-42322-7
  • Khan J.A., Westphal C., Ghamri-Doudane Y., 2018. A Popularity-aware Centrality Metric for Content Placement in Information Centric Networks. 2018 International Conference on Computing, Networking and Communications, ICNC 2018, , pp.554-560.DOI: 10.1109/ICCNC.2018.8390396
  • Freeman, Linton. 1977. A set of measures of centrality based on betweenness. Sociometry. 40 (1): 35–41.DOI: 10.2307/3033543
  • Oggier F., Phetsouvanh S., Datta A., 2019. A split-and-transfer flow based entropic centrality. PeerJ Computer Science, 2019(9).DOI: 10.7717/peerj-cs.220
  • Faghani M., Nguyen U., 2013. A study of xss worm propagation and detection mechanisms in online social networks. IEEE Transactions on Information Forensics and Security, 8(11), pp.1815-1826.DOI: 10.1109/TIFS.2013.2280884
  • Zhou H., Ruan M., Zhu C., Leung V.C.M., Xu S., Huang C.M., 2018. A Time-Ordered Aggregation Model-Based Centrality Metric for Mobile Social Networks. IEEE Access, 6, pp.25588-25599.DOI: 10.1109/ACCESS.2018.2831247
  • Estrada E., Hatano N., 2010. A vibrational approach to node centrality and vulnerability in complex networks. Physica A: Statistical Mechanics and its Applications, 389(17), pp.3648-3660.DOI: 10.1016/j.physa.2010.03.030
  • Sun H.l., Chen D.b., He J.l., Ch\'ng E., 2019. A voting approach to uncover multiple influential spreaders on weighted networks. Physica A: Statistical Mechanics and its Applications, 519, pp.303-312.DOI: 10.1016/j.physa.2018.12.001
  • Donato C., Lo Giudice P., Marretta R., Ursino D., Virgili L., 2019. A well-tailored centrality measure for evaluating patents and their citations. Journal of Documentation, 75(4), pp.750-772.DOI: 10.1108/JD-10-2018-0168
  • Riondato M., Upfal E., 2018. ABRA: Approximating betweenness centrality in static and dynamic graphs with rademacher averages. ACM Transactions on Knowledge Discovery from Data, 12(5).DOI: 10.1145/3208351
  • Mavroforakis C., Mathioudakis M., Gionis A., 2016. Absorbing random-walk centrality: Theory and algorithms. Proceedings - IEEE International Conference on Data Mining, ICDM, 2016-January, pp.901-906.DOI: 10.1109/ICDM.2015.103
  • Li J., Dueñas-Osorio L., Chen C., Shi C., 2017. AC power flow importance measures considering multi-element failures. Reliability Engineering and System Safety, 160, pp.89-97.DOI: 10.1016/j.ress.2016.11.010
  • Liu G., Yao X., Luo Z., Kang S., Long W., Fan Q., Gao P., 2019. Agglomeration centrality to examine spatial scaling law in cities. Computers, Environment and Urban Systems, 77.DOI: 10.1016/j.compenvurbsys.2019.101357
  • Oliva G., Esposito Amideo A., Starita S., Setola R., Scaparra M.P., 2020. Aggregating centrality rankings: A novel approach to detect critical infrastructure vulnerabilities. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11777 LNCS, pp.57-68.DOI: 10.1007/978-3-030-37670-3_5
  • Kirkland S., 2010. Algebraic connectivity for vertex-deleted subgraphs, and a notion of vertex centrality. Discrete Mathematics, 310(4), pp.911-921.DOI: 10.1016/j.disc.2009.10.011
  • White S., Smyth P., 2003. Algorithms for estimating relative importance in networks. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, , pp.266-275.DOI: 10.1145/956750.956782
  • Avrachenkov K., Litvak N., Medyanikov V., Sokol M., 2013. Alpha current flow betweenness centrality. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8305 LNCS, pp.106-117.DOI: 10.1007/978-3-319-03536-9_9
  • Agryzkov T., Oliver J., Tortosa L., Vicent J., 2012. An algorithm for ranking the nodes of an urban network based on the concept of PageRank vector. Applied Mathematics and Computation, 219(4), pp.2186-2193.DOI: 10.1016/j.amc.2012.08.064
  • Pedroche F., Tortosa L., Vicent J.F., 2019. An eigenvector centrality for multiplex networks with data. Symmetry, 11(6).DOI: 10.3390/sym11060763
  • Espejo R., Lumbreras S., Ramos A., Huang T., Bompard E., 2019. An extended metric for the analysis of power-network vulnerability: The line electrical centrality. 2019 IEEE Milan PowerTech, PowerTech 2019, .DOI: 10.1109/PTC.2019.8810514
  • MacKer J., 2016. An improved local bridging centrality model for distributed network analytics. Proceedings - IEEE Military Communications Conference MILCOM, , pp.600-605.DOI: 10.1109/MILCOM.2016.7795393
  • Sun H., Liang Y., Chen L., Wang Y., Du W., Shi X., 2013. An improved sum of edge clustering coefficient method for essential protein identification. Journal of Bionanoscience, 7(4), pp.386-390.DOI: 10.1166/jbns.2013.1152
  • Ortiz-Arroyo D., Hussain D., 2008. An information theory approach to identify sets of key players. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5376 LNCS, pp.15-26.DOI: 10.1007/978-3-540-89900-6_5
  • Fontugne R., Shah A., Aben E., 2017. AS hegemony: A robust metric for as centrality. SIGCOMM Posters and Demos 2017 - Proceedings of the 2017 SIGCOMM Posters and Demos, Part of SIGCOMM 2017, , pp.48-50.DOI: 10.1145/3123878.3131982
  • Cickovski T., Peake E., Aguiar-Pulido V., Narasimhan G., 2017. ATria: A novel centrality algorithm applied to biological networks. BMC Bioinformatics, 18.DOI: 10.1186/s12859-017-1659-z
  • Skibski O., Rahwan T., Michalak T., Yokoo M., 2019. Attachment centrality: Measure for connectivity in networks. Artificial Intelligence, 274, pp.151-179.DOI: 10.1016/j.artint.2019.03.002
  • Kleinberg, J.M., 1998, January. Authoritative sources in a hyperlinked environment. In Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms (pp. 668-677).
  • Kleinberg J.M., 1999. Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5), pp.604-632.DOI: 10.1145/324133.324140
  • Avrachenkov K., Mazalov V., Tsynguev B., 2015. Beta current flow centrality for weighted networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9197, pp.216-227.DOI: 10.1007/978-3-319-21786-4_19
  • Ghalmane Z., Hassouni M.E., Cherifi H., 2018. Betweenness Centrality for Networks with Non-Overlapping Community Structure. 2018 IEEE Workshop on Complexity in Engineering, COMPENG 2018, .DOI: 10.1109/CompEng.2018.8536229
  • Zhai L., Yan X., Zhang G., 2018. Bi-directional h-index: A new measure of node centrality in weighted and directed networks. Journal of Informetrics, 12(1), pp.299-314.DOI: 10.1016/j.joi.2018.01.004
  • Yi, Y., Shan, L., Li, H. and Zhang, Z., 2018, July. Biharmonic distance related centrality for edges in weighted networks. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (pp. 3620-3626).
  • He X., Gao M., Kan M.Y., Wang D., 2017. BiRank: Towards Ranking on Bipartite Graphs. IEEE Transactions on Knowledge and Data Engineering, 29(1), pp.57-71.DOI: 10.1109/TKDE.2016.2611584
  • Salavati C., Abdollahpouri A., Manbari Z., 2018. BridgeRank: A novel fast centrality measure based on local structure of the network. Physica A: Statistical Mechanics and its Applications, 496, pp.635-653.DOI: 10.1016/j.physa.2017.12.087
  • Hwang W., Kim T., Ramanathan M., Zhang A., 2008. Bridging centrality: Graph mining from element level to group level. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, , pp.336-344.DOI: 10.1145/1401890.1401934
  • Bonacich P., Lloyd P., 2004. Calculating status with negative relations. Social Networks, 26(4), pp.331-338.DOI: 10.1016/j.socnet.2004.08.007
  • Everett M., Borgatti S., 2012. Categorical attribute based centrality: E-I and G-F centrality. Social Networks, 34(4), pp.562-569.DOI: 10.1016/j.socnet.2012.06.002
  • Caporossi, G., Paiva, M., Vukičevic, D. and Segatto, M., 2012. Centrality and betweenness: vertex and edge decomposition of the Wiener index. MATCH-Communications in Mathematical and Computer Chemistry, 68(1), p.293.
  • Ghalmane Z., Cherifi C., Cherifi H., Hassouni M.E., 2019. Centrality in Complex Networks with Overlapping Community Structure. Scientific Reports, 9(1).DOI: 10.1038/s41598-019-46507-y
  • Freeman L., 1978. Centrality in social networks conceptual clarification. Social Networks, 1(3), pp.215-239.DOI: 10.1016/0378-8733(78)90021-7
  • Freeman L., Borgatti S., White D., 1991. Centrality in valued graphs: A measure of betweenness based on network flow. Social Networks, 13(2), pp.141-154.DOI: 10.1016/0378-8733(91)90017-N
  • Izaac J.A., Zhan X., Bian Z., Wang K., Li J., Wang J.B., Xue P., 2017. Centrality measure based on continuous-time quantum walks and experimental realization. Physical Review A, 95(3).DOI: 10.1103/PhysRevA.95.032318
  • Riquelme F., Gonzalez-Cantergiani P., Molinero X., Serna M., 2018. Centrality measure in social networks based on linear threshold model. Knowledge-Based Systems, 140, pp.92-102.DOI: 10.1016/j.knosys.2017.10.029
  • Brandes U., Fleischer D., 2005. Centrality measures based on current flow. Lecture Notes in Computer Science, 3404, pp.533-544.DOI: 10.1007/978-3-540-31856-9_44
  • De la Cruz Cabrera O., Matar M., Reichel L., 2021. Centrality measures for node-weighted networks via line graphs and the matrix exponential. Numerical Algorithms, .DOI: 10.1007/s11075-020-01050-0
  • Aleskerov, F.T., Meshcheryakova, N. and Shvydun, S., 2016. Centrality measures in networks based on nodes attributes, long-range interactions and group influence. Long-Range Interactions and Group Influence.DOI: 10.2139/ssrn.3196962
  • Yazici, M. and Sarac, M., 2015. Centrality measures with a new index called E-User (Effective User) Index for determiningthe most effective user in Twitter Online Social Network. International Journal on Computer Science and Engineering, 7(1), p.1.
  • Pal S., Kundu S., Murthy C., 2014. Centrality measures, upper bound, and influence maximization in large scale directed social networks. Fundamenta Informaticae, 130(3), pp.317-342.DOI: 10.3233/FI-2014-994
  • Ding C., Li K., 2018. Centrality ranking in multiplex networks using topologically biased random walks. Neurocomputing, 312, pp.263-275.DOI: 10.1016/j.neucom.2018.05.109
  • Zhang G., Liu L., Feng Y., Shao Z., Li Y., 2014. Cext-N index: a network node centrality measure for collaborative relationship distribution. Scientometrics, 101(1), pp.291-307.DOI: 10.1007/s11192-014-1358-8
  • Yao Y., Xiao X., Zhang C., Xia S., 2018. Classifying quality centrality for source localization in social networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10966 LNCS, pp.295-307.DOI: 10.1007/978-3-319-94289-6_19
  • Şimşek M., Meyerhenke H., 2020. Combined centrality measures for an improved characterization of influence spread in social networks. Journal of Complex Networks, 8(1).DOI: 10.1093/comnet/cnz048
  • Agryzkov T., Pedroche F., Tortosa L., Vicent J.F., 2018. Combining the two-layers pageRank approach with the APA centrality in networks with data. ISPRS International Journal of Geo-Information, 7(12).DOI: 10.3390/ijgi7120480
  • Estrada E., Higham D.J., Hatano N., 2009. Communicability betweenness in complex networks. Physica A: Statistical Mechanics and its Applications, 388(5), pp.764-774.DOI: 10.1016/j.physa.2008.11.011
  • Bavelas A., 1950. Communication Patterns in Task-Oriented Groups. Journal of the Acoustical Society of America, 22(6), pp.725-730.DOI: 10.1121/1.1906679
  • Li X., Zhou S., Liu J., Lian G., Chen G., Lin C.W., 2019. Communities detection in social network based on local edge centrality. Physica A: Statistical Mechanics and its Applications, 531.DOI: 10.1016/j.physa.2019.121552
  • Das K., Samanta S., De K., Pal M., 2020. Complete neighbourhood centrality and its application. 4th International Conference on Computational Intelligence and Networks, CINE 2020, .DOI: 10.1109/CINE48825.2020.234386
  • Joseph A., Chen G., 2014. Composite centrality: A natural scale for complex evolving networks. Physica D: Nonlinear Phenomena, 267, pp.58-67.DOI: 10.1016/j.physd.2013.08.005
  • Gao, S. and Caines, P.E., 2018, July. Consensus-induced Centrality for Networks of Dynamical Systems. In Proceedings of the 23rd International Symposium on Mathematical Theory of Networks and Systems, Hong Kong, China (pp. 769-775).
  • Fushimi T., Satoh T., Saito K., Kazama K., Kando N., 2016. Content centrality measure for networks: Introducing distance-based Decay weights. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10047 LNCS, pp.40-54.DOI: 10.1007/978-3-319-47874-6_4
  • Liu Y., Slotine J., Barabási A., 2012. Control Centrality and Hierarchical Structure in Complex Networks. PLoS ONE, 7(9).DOI: 10.1371/journal.pone.0044459
  • Li C., Li Q., Van Mieghem P., Stanley H.E., Wang H., 2015. Correlation between centrality metrics and their application to the opinion model. European Physical Journal B, 88(3), pp.1-13.DOI: 10.1140/epjb/e2015-50671-y
  • Ovelgönne M., Kang C., Sawant A., Subrahmanian V., 2012. Covertness centrality in networks. Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012, , pp.863-870.DOI: 10.1109/ASONAM.2012.156
  • Ibrahim M.H., Missaoui R., Vaillancourt J., 2020. Cross-Face Centrality: A New Measure for Identifying Key Nodes in Networks Based on Formal Concept Analysis. IEEE Access, 8, pp.206901-206913.DOI: 10.1109/ACCESS.2020.3038306
  • Chakraborty T., Narayanam R., 2016. Cross-layer betweenness centrality in multiplex networks with applications. 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016, , pp.397-408.DOI: 10.1109/ICDE.2016.7498257
  • Ma Y., Liu M., Zhang P., Qi X., 2018. CS-TOTR: A new vertex centrality method for directed signed networks based on status theory. International Journal of Modern Physics C, 29(5).DOI: 10.1142/S0129183118400028
  • Zhou X., Liang X., Zhao J., Zhang S., 2018. Cycle Based Network Centrality. Scientific Reports, 8(1).DOI: 10.1038/s41598-018-30249-4
  • Giscard P., Wilson R., 2018. Cycle-centrality in economic and biological networks. Studies in Computational Intelligence, 689, pp.14-28.DOI: 10.1007/978-3-319-72150-7_2
  • Fan M., Cao Z., Cheng J., Yang F., Qi X., 2020. Degree-like centrality with structural zeroes or ones: When is a neighbor not a neighbor?. Social Networks, 63, pp.38-46.DOI: 10.1016/j.socnet.2020.05.002
  • Cheng Y., Lee R., Lim E., Zhu F., 2013. DelayFlow centrality for identifying critical nodes in transportation networks. Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013, , pp.1462-1463.DOI: 10.1145/2492517.2492595
  • Ibnoulouafi A., El Haziti M., 2018. Density centrality: identifying influential nodes based on area density formula. Chaos, Solitons and Fractals, 114, pp.69-80.DOI: 10.1016/j.chaos.2018.06.022
  • Zhang W., Xu J., Li Y., Zou X., 2018. Detecting Essential Proteins Based on Network Topology, Gene Expression Data, and Gene Ontology Information. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 15(1), pp.109-116.DOI: 10.1109/TCBB.2016.2615931
  • Jensen P., Morini M., Karsai M., Venturini T., Vespignani A., Jacomy M., Cointet J.P., Mercklé P., Fleury E., 2016. Detecting global bridges in networks. Journal of Complex Networks, 4(3), pp.319-329.DOI: 10.1093/comnet/cnv022
  • Roohi L., Rubinstein B.I.P., Teague V., 2019. Differentially-Private Two-Party Egocentric Betweenness Centrality. Proceedings - IEEE INFOCOM, 2019-April(), pp.2233-2241.DOI: 10.1109/INFOCOM.2019.8737405
  • Mistry D., Wise R.P., Dickerson J.A., 2017. DiffSLC: A graph centrality method to detect essential proteins of a protein-protein interaction network. PLoS ONE, 12(11).DOI: 10.1371/journal.pone.0187091
  • Kang C., Kraus S., Molinaro C., Spezzano F., Subrahmanian V., 2016. Diffusion centrality: A paradigm to maximize spread in social networks. Artificial Intelligence, 239, pp.70-96.DOI: 10.1016/j.artint.2016.06.008
  • Stella M., De Domenico M., 2018. Distance entropy cartography characterises centrality in complex networks. Entropy, 20(4).DOI: 10.3390/e20040268
  • Fronzetti Colladon A., Naldi M., 2020. Distinctiveness centrality in social networks. PLoS ONE, 15(5).DOI: 10.1371/journal.pone.0233276
  • Lulli A., Ricci L., Carlini E., Dazzi P., 2015. Distributed Current Flow Betweenness Centrality. International Conference on Self-Adaptive and Self-Organizing Systems, SASO, 2015-October, pp.71-80.DOI: 10.1109/SASO.2015.15
  • Milenković T., Memišević V., Bonato A., Pržulj N., 2011. Dominating biological networks. PLoS ONE, 6(8).DOI: 10.1371/journal.pone.0023016
  • Huang D., Yu Z., 2017. Dynamic-Sensitive centrality of nodes in temporal networks. Scientific Reports, 7.DOI: 10.1038/srep41454
  • Hage P., Harary F., 1995. Eccentricity and centrality in networks. Social Networks, 17(1), pp.57-63.DOI: 10.1016/0378-8733(94)00248-9
  • Lockhart J., Minello G., Rossi L., Severini S., Torsello A., 2016. Edge centrality via the Holevo quantity. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10029 LNCS, pp.143-152.DOI: 10.1007/978-3-319-49055-7_13
  • Sarmento R.P., Cordeiro M., Brazdil P., Gama J., 2018. Efficient incremental laplace centrality algorithm for dynamic networks. Studies in Computational Intelligence, 689, pp.341-352.DOI: 10.1007/978-3-319-72150-7_28
  • Chen C., Wang W., Wang X., 2016. Efficient maximum closeness centrality group identification. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9877 LNCS, pp.43-55.DOI: 10.1007/978-3-319-46922-5_4
  • Everett M., Borgatti S.P., 2005. Ego network betweenness. Social Networks, 27(1), pp.31-38.DOI: 10.1016/j.socnet.2004.11.007
  • Ghanem M., Coriat F., Tabourier L., 2017. Ego-betweenness centrality in link streams. Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017, , pp.667-674.DOI: 10.1145/3110025.3110158
  • Zareie A., Sheikhahmadi A., 2019. EHC: Extended H-index Centrality measure for identification of users’ spreading influence in complex networks. Physica A: Statistical Mechanics and its Applications, 514, pp.141-155.DOI: 10.1016/j.physa.2018.09.064
  • Huang X., Huang W., 2019. Eigenedge: A measure of edge centrality for big graph exploration. Journal of Computer Languages, 55.DOI: 10.1016/j.cola.2019.100925
  • Lv L., Zhang K., Zhang T., Li X., Zhang J., Xue W., 2019. Eigenvector centrality measure based on node similarity for multilayer and temporal networks. IEEE Access, 7, pp.115725-115733.DOI: 10.1109/ACCESS.2019.2936217
  • Bonacich P., Lloyd P., 2001. Eigenvector-like measures of centrality for asymmetric relations. Social Networks, 23(3), pp.191-201.DOI: 10.1016/S0378-8733(01)00038-7
  • Giustolisi O., Ridolfi L., Simone A., 2020. Embedding the intrinsic relevance of vertices in network analysis: the case of centrality metrics. Scientific Reports, 10(1).DOI: 10.1038/s41598-020-60151-x
  • Puzis R., Sofer Z., Cohen D., Hugi M., 2018. Embedding-centrality: Generic centrality computation using neural networks. Springer Proceedings in Complexity, (219279), pp.87-97.DOI: 10.1007/978-3-319-73198-8_8
  • Lu P., Dong C., 2020. EMH: Extended Mixing H-index centrality for identification important users in social networks based on neighborhood diversity. Modern Physics Letters B, 34(26).DOI: 10.1142/S021798492050284X
  • Kong, R., Han, C., Guo, T. and Pei, W., 2013. An Energy-Based Centrality for Electrical Networks. Energy and Power Engineering, 5(04), p.597.DOI: 10.4236/epe.2013.54B115
  • Šikić M., Lančić A., Antulov-Fantulin N., Štefančić H., 2013. Epidemic centrality - Is there an underestimated epidemic impact of network peripheral nodes?. European Physical Journal B, 86(10).DOI: 10.1140/epjb/e2013-31025-5
  • Parvandeh S., McKinney B.A., 2019. Epistasisrank and Epistasiskatz: Interaction network centrality methods that integrate prior knowledge networks. Bioinformatics, 35(13), pp.2329-2331.DOI: 10.1093/bioinformatics/bty965
  • Huang, S., Cui, H. and Ding, Y., 2014. Evaluation of node importance in complex networks. arXiv preprint arXiv:1402.5743.
  • Natale F., Savini L., Giovannini A., Calistri P., Candeloro L., Fiore G., 2011. Evaluation of risk and vulnerability using a Disease Flow Centrality measure in dynamic cattle trade networks. Preventive Veterinary Medicine, 98(2-3), pp.111-118.DOI: 10.1016/j.prevetmed.2010.11.013
  • Agryzkov T., Curado M., Pedroche F., Tortosa L., Vicent J.F., 2019. Extending the adapted PageRank algorithm centrality to multiplex networks with data using the PageRank two-layer approach. Symmetry, 11(2).DOI: 10.3390/sym11020284
  • Newman M.E.J., Girvan M., 2004. Finding and evaluating community structure in networks. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 69(2 2).DOI: 10.1103/PhysRevE.69.026113
  • Newman M.E.J., 2006. Finding community structure in networks using the eigenvectors of matrices. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 74(3).DOI: 10.1103/PhysRevE.74.036104
  • Zareie A., Sheikhahmadi A., Jalili M., Fasaei M.S.K., 2020. Finding influential nodes in social networks based on neighborhood correlation coefficient. Knowledge-Based Systems, 194.DOI: 10.1016/j.knosys.2020.105580
  • Tew K.L., Li X.L., Tan S.H., 2007. Functional centrality: detecting lethality of proteins in protein interaction networks.. Genome informatics. International Conference on Genome Informatics, 19, pp.166-177.DOI: 10.1142/9781860949852_0015
  • Pržulj N., Wigle D., Jurisica I., 2004. Functional topology in a network of protein interactions. Bioinformatics, 20(3), pp.340-348.DOI: 10.1093/bioinformatics/btg415
  • Tavassoli S., Zweig K.A., 2017. Fuzzy centrality evaluation in complex and multiplex networks. Springer Proceedings in Complexity, , pp.31-43.DOI: 10.1007/978-3-319-54241-6_3
  • Nepusz T., Petróczi A., Négyessy L., Bazsó F., 2008. Fuzzy communities and the concept of bridgeness in complex networks. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 77(1).DOI: 10.1103/PhysRevE.77.016107
  • Singh R., Chakraborty A., Manoj B., 2017. GFT centrality: A new node importance measure for complex networks. Physica A: Statistical Mechanics and its Applications, 487, pp.185-195.DOI: 10.1016/j.physa.2017.06.018
  • Kolaczyk E., Chua D., Barthélemy M., 2009. Group betweenness and co-betweenness: Inter-related notions of coalition centrality. Social Networks, 31(3), pp.190-203.DOI: 10.1016/j.socnet.2009.02.003
  • Zhao S., Rousseau R., Ye F., 2011. H-Degree as a basic measure in weighted networks. Journal of Informetrics, 5(4), pp.668-677.DOI: 10.1016/j.joi.2011.06.005
  • Marchiori M., Latora V., 2000. Harmony in the small-world. Physica A: Statistical Mechanics and its Applications, 285(3), pp.539-546.DOI: 10.1016/S0378-4371(00)00311-3
  • Duron C., 2020. Heatmap centrality: A new measure to identify super-spreader nodes in scale-free networks. PLoS ONE, 15(7 July).DOI: 10.1371/journal.pone.0235690
  • Taheri S.M., Mahyar H., Firouzi M., Ghalebi E., Grosu R., Movaghar A., 2017. HellRank: a Hellinger-based centrality measure for bipartite social networks. Social Network Analysis and Mining, 7(1).DOI: 10.1007/s13278-017-0440-7
  • del Rio G., Koschützki D., Coello G., 2009. How to identify essential genes from molecular networks?. BMC Systems Biology, 3, pp.102.DOI: 10.1186/1752-0509-3-102
  • Qiao T., Shan W., Zhou C., 2017. How to identify the most powerful node in complex networks? A novel entropy centrality approach. Entropy, 19(11).DOI: 10.3390/e19110614
  • Lin C., Chin C., Wu H., Chen S., Ho C., Ko M., 2008. Hubba: hub objects analyzer--a framework of interactome hubs identification for network biology.. Nucleic acids research, 36(Web Server issue).DOI: 10.1093/nar/gkn257
  • Singh A., Singh R., Iyengar S., 2019. Hybrid centrality measures for service coverage problem. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11917 LNCS, pp.81-94.DOI: 10.1007/978-3-030-34980-6_11
  • Stai E., Sotiropoulos K., Karyotis V., Papavassiliou S., 2017. Hyperbolic Embedding for Efficient Computation of Path Centralities and Adaptive Routing in Large-Scale Complex Commodity Networks. IEEE Transactions on Network Science and Engineering, 4(3), pp.140-153.DOI: 10.1109/TNSE.2017.2690258
  • Stai E., Sotiropoulos K., Karyotis V., Papavassiliou S., 2016. Hyperbolic Traffic Load Centrality for large-scale complex communications networks. 2016 23rd International Conference on Telecommunications, ICT 2016, .DOI: 10.1109/ICT.2016.7500371
  • Zhao J., Wang P., Lui J.C.S., Towsley D., Guan X., 2017. I/O-efficient calculation of H-group closeness centrality over disk-resident graphs. Information Sciences, 400-401, pp.105-128.DOI: 10.1016/j.ins.2017.03.017
  • Wang P., Yu X., Lü J., 2014. Identification and evolution of structurally dominant nodes in protein-protein interaction networks. IEEE Transactions on Biomedical Circuits and Systems, 8(1), pp.87-97.DOI: 10.1109/TBCAS.2014.2303160
  • Wang J., Li M., Wang H., Pan Y., 2012. Identification of essential proteins based on edge clustering coefficient. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 9(4), pp.1070-1080.DOI: 10.1109/TCBB.2011.147
  • Wang Y., Sun H., Du W., Blanzieri E., Viero G., Xu Y., Liang Y., 2014. Identification of essential proteins based on ranking Edge-Weights in Protein-Protein Interaction networks. PLoS ONE, 9(9).DOI: 10.1371/journal.pone.0108716
  • Vogiatzis, C. and Camur, M.C., 2019. Identification of essential proteins using induced stars in protein–protein interaction networks. INFORMS Journal on Computing, 31(4), pp.703-718.DOI: 10.1287/ijoc.2018.0872
  • Wang P., Lü J., Yu X., 2014. Identification of important nodes in directed biological networks: A network motif approach. PLoS ONE, 9(8).DOI: 10.1371/journal.pone.0106132
  • Zhang J., Chen D., Dong Q., Zhao Z., 2016. Identifying a set of influential spreaders in complex networks. Scientific Reports, 6.DOI: 10.1038/srep27823
  • Bae J., Kim S., 2014. Identifying and ranking influential spreaders in complex networks by neighborhood coreness. Physica A: Statistical Mechanics and its Applications, 395, pp.549-559.DOI: 10.1016/j.physa.2013.10.047
  • Chen D., Lü L., Shang M., Zhang Y., Zhou T., 2012. Identifying influential nodes in complex networks. Physica A: Statistical Mechanics and its Applications, 391(4), pp.1777-1787.DOI: 10.1016/j.physa.2011.09.017
  • Chen X., Tan M., Zhao J., Yang T., Wu D., Zhao R., 2019. Identifying influential nodes in complex networks based on a spreading influence related centrality. Physica A: Statistical Mechanics and its Applications, 536.DOI: 10.1016/j.physa.2019.122481
  • Chen D.B., Gao H., Lü L., Zhou T., 2013. Identifying influential nodes in large-scale directed networks: The role of clustering. PLoS ONE, 8(10).DOI: 10.1371/journal.pone.0077455
  • Kumar S., Panda B.S., 2020. Identifying influential nodes in Social Networks: Neighborhood Coreness based voting approach. Physica A: Statistical Mechanics and its Applications, 553.DOI: 10.1016/j.physa.2020.124215
  • Li Q., Zhou T., Lü L., Chen D., 2014. Identifying influential spreaders by weighted LeaderRank. Physica A: Statistical Mechanics and its Applications, 404, pp.47-55.DOI: 10.1016/j.physa.2014.02.041
  • Ma L.L., Ma C., Zhang H.F., Wang B.H., 2016. Identifying influential spreaders in complex networks based on gravity formula. Physica A: Statistical Mechanics and its Applications, 451, pp.205-212.DOI: 10.1016/j.physa.2015.12.162
  • Liu H., Ma C., Xiang B., Tang M., Zhang H., 2018. Identifying multiple influential spreaders based on generalized closeness centrality. Physica A: Statistical Mechanics and its Applications, 492, pp.2237-2248.DOI: 10.1016/j.physa.2017.11.138
  • Williams, J., 2019. Identifying sensitive components in infrastructure networks via critical flows. engrXiv.
  • Li X., Liu Y., Jiang Y., Liu X., 2016. Identifying social influence in complex networks: A novel conductance eigenvector centrality model. Neurocomputing, 210, pp.141-154.DOI: 10.1016/j.neucom.2015.11.123
  • Lee T., Lee H., Hwang K., 2013. Identifying superspreaders for epidemics using R0-adjusted network centrality. Proceedings of the 2013 Winter Simulation Conference - Simulation: Making Decisions in a Complex World, WSC 2013, , pp.2239-2249.DOI: 10.1109/WSC.2013.6721600
  • Salavaty, Abbas and Ramialison, Mirana and Currie, Peter D., IHS; An Integrative Method for the Identification of Network Hubs. Available at SSRN: https://ssrn.com/abstract=3565980 or http://dx.doi.org/10.2139/ssrn.3565980 DOI: 10.2139/ssrn.3565980
  • Wang J., Li C., Xia C., 2018. Improved centrality indicators to characterize the nodal spreading capability in complex networks. Applied Mathematics and Computation, 334, pp.388-400.DOI: 10.1016/j.amc.2018.04.028
  • Shao H., Mesbahi M., Li D., Xi Y., 2017. Inferring centrality from network snapshots. Scientific Reports, 7.DOI: 10.1038/srep40642
  • Iannelli F., Mariani M., Sokolov I., 2018. Influencers identification in complex networks through reaction-diffusion dynamics. Physical Review E, 98(6).DOI: 10.1103/PhysRevE.98.062302
  • Wang Y., Chen B., Li W., Zhang D., 2019. Influential Node Identification in Command and Control Networks Based on Integral k-Shell. Wireless Communications and Mobile Computing, 2019.DOI: 10.1155/2019/6528431
  • Dai Z., Li P., Chen Y., Zhang K., Zhang J., 2019. Influential node ranking via randomized spanning trees. Physica A: Statistical Mechanics and its Applications, 526.DOI: 10.1016/j.physa.2019.02.047
  • Salavaty, A., Ramialison, M. and Currie, P.D., 2020. Integrated value of influence: an integrative method for the identification of the most influential nodes within networks. Patterns, 1(5), p.100052.DOI: 10.1016/j.patter.2020.100052
  • Valente T., Foreman R., 1998. Integration and radiality: Measuring the extent of an individual\'s connectedness and reachability in a network. Social Networks, 20(1), pp.89-105.DOI: 10.1016/S0378-8733(97)00007-5
  • Prifti E., Zucker J.D., Clément K., Henegar C., 2010. Interactional and functional centrality in transcriptional co-expression networks. Bioinformatics, 26(24), pp.3083-3089.DOI: 10.1093/bioinformatics/btq591
  • Tsiotas D., Polyzos S., 2015. Introducing a new centrality measure from the transportation network analysis in Greece. Annals of Operations Research, 227(1), pp.93-117.DOI: 10.1007/s10479-013-1434-0
  • Agha Mohammad Ali Kermani M., Badiee A., Aliahmadi A., Ghazanfari M., Kalantari H., 2016. Introducing a procedure for developing a novel centrality measure (Sociability Centrality) for social networks using TOPSIS method and genetic algorithm. Computers in Human Behavior, 56, pp.295-305.DOI: 10.1016/j.chb.2015.11.008
  • Xu S., Wang P., Lü J., 2017. Iterative Neighbour-Information Gathering for Ranking Nodes in Complex Networks. Scientific Reports, 7.DOI: 10.1038/srep41321
  • Niu J., Fan J., Wang L., Stojinenovic M., 2014. K-hop centrality metric for identifying influential spreaders in dynamic large-scale social networks. 2014 IEEE Global Communications Conference, GLOBECOM 2014, , pp.2954-2959.DOI: 10.1109/GLOCOM.2014.7037257
  • Alahakoon T., Tripathi R., Kourtellis N., Simha R., Iamnitchi A., 2011. K-path centrality: A new centrality measure in social networks. Proceedings of the 4th Workshop on Social Network Systems, SNS\'11, .DOI: 10.1145/1989656.1989657
  • Akgün M.K., Tural M.K., 2020. k-step betweenness centrality. Computational and Mathematical Organization Theory, 26(1), pp.55-87.DOI: 10.1007/s10588-019-09301-9
  • Aleskerov F., Andrievskaya I., Permjakova E., 2016. Key borrowers detected by the intensities of their short-range interactions. Springer Proceedings in Mathematics and Statistics, 156, pp.267-280.DOI: 10.1007/978-3-319-29608-1_18
  • Mazalov V., Tsynguev B., 2016. Kirchhoff centrality measure for collaboration network. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9795, pp.147-157.DOI: 10.1007/978-3-319-42345-6_13
  • Li H., Zhang Z., 2018. Kirchhoff index as a measure of edge centrality in weighted networks: Nearly linear time algorithms. Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms, , pp.2377-2396.DOI: 10.1137/1.9781611975031.153
  • Shanahan M., Wildie M., 2012. Knotty-centrality: Finding the connective core of a complex network. PLoS ONE, 7(5).DOI: 10.1371/journal.pone.0036579
  • Jian, X., 2014. KSC centralized index model in complex network. Journal of Networks, 9(5), p.1245.
  • Qi X., Fuller E., Wu Q., Wu Y., Zhang C., 2012. Laplacian centrality: A new centrality measure for weighted networks. Information Sciences, 194, pp.240-253.DOI: 10.1016/j.ins.2011.12.027
  • Garzon C., Pavas A., 2017. Laplacian eigenvector centrality as tool for assessing causality in power quality. 2017 IEEE Manchester PowerTech, Powertech 2017, .DOI: 10.1109/PTC.2017.7981261
  • Lü L., Zhang Y., Yeung C., Zhou T., 2011. Leaders in social networks, the delicious case. PLoS ONE, 6(6).DOI: 10.1371/journal.pone.0021202
  • Lee K.H., Kim M.H., 2020. Linearization of dependency and sampling for participation-based betweenness centrality in very large b-hypergraphs. ACM Transactions on Knowledge Discovery from Data, 14(3).DOI: 10.1145/3375399
  • Zhang Q., Karsai M., Vespignani A., 2018. Link transmission centrality in large-scale social networks. EPJ Data Science, 7(1).DOI: 10.1140/epjds/s13688-018-0162-8
  • Korn A., Schubert A., Telcs A., 2009. Lobby index in networks. Physica A: Statistical Mechanics and its Applications, 388(11), pp.2221-2226.DOI: 10.1016/j.physa.2009.02.013
  • Piraveenan M., Prokopenko M., Zomaya A., 2008. Local assortativeness in scale-free networks. EPL, 84(2).DOI: 10.1209/0295-5075/84/28002
  • Nathan E., Zakrzewska A., Riedy J., Bader D., 2017. Local community detection in dynamic graphs using personalized centrality. Algorithms, 10(3).DOI: 10.3390/a10030102
  • Martin T., Zhang X., Newman M.E.J., 2014. Localization and centrality in networks. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 90(5).DOI: 10.1103/PhysRevE.90.052808
  • Liu J., Lin J., Guo Q., Zhou T., 2016. Locating influential nodes via dynamics-sensitive centrality. Scientific Reports, 6.DOI: 10.1038/srep21380
  • Ibnoulouafi A., El Haziti M., Cherifi H., 2018. M-Centrality: Identifying key nodes based on global position and local degree variation. Journal of Statistical Mechanics: Theory and Experiment, 2018(7).DOI: 10.1088/1742-5468/aace08
  • Wang G., Shen Y., Luan E., 2008. Measure of centrality based on modularity matrix. Progress in Natural Science, 18(8), pp.1043-1047.DOI: 10.1016/j.pnsc.2008.03.015
  • Rossi L., Torsello A., 2017. Measuring vertex centrality using the Holevo quantity. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10310 LNCS, pp.154-164.DOI: 10.1007/978-3-319-58961-9_14
  • Herzog S.M., Hills T.T., 2019. Mediation Centrality in Adversarial Policy Networks. Complexity, 2019.DOI: 10.1155/2019/1918504
  • Pontiveros B.B.F., Steichen M., State R., 2019. Mint Centrality: A Centrality Measure for the Bitcoin Transaction Graph. ICBC 2019 - IEEE International Conference on Blockchain and Cryptocurrency, , pp.159-162.DOI: 10.1109/BLOC.2019.8751401
  • Masaaki Miyashita and Norihiko Shinomiya. 2015, Modified Betweenness Centrality to Identify Relay Nodes in Data Networks. ACHI 2015 : The Eighth International Conference on Advances in Computer-Human Interactions.
  • Magelinski, T., Bartulovic, M. and Carley, K.M., 2020. Modularity-Impact: a Signed Group Centrality Measure for Complex Networks. arXiv preprint arXiv:2003.00056.
  • Ivanov S., Gorlushkina N., Ivanova L., 2018. Multi-parametric centrality method for graph network models. AIP Conference Proceedings, 1952.DOI: 10.1063/1.5032005
  • Brandes, U., 2005. Network analysis: methodological foundations (Vol. 3418). Springer Science & Business Media.
  • Sinclair P., 2009. Network centralization with the Gil Schmidt power centrality index. Social Networks, 31(3), pp.214-219.DOI: 10.1016/j.socnet.2009.04.004
  • Chanekar P.V., Nozari E., Cortes J., 2019. Network Modification using a Novel Gramian-based Edge Centrality. Proceedings of the IEEE Conference on Decision and Control, 2019-December, pp.1686-1691.DOI: 10.1109/CDC40024.2019.9028860
  • Seidman S., 1983. Network structure and minimum degree. Social Networks, 5(3), pp.269-287.DOI: 10.1016/0378-8733(83)90028-X
  • Everett M., Borgatti S., 2014. Networks containing negative ties. Social Networks, 38(1), pp.111-120.DOI: 10.1016/j.socnet.2014.03.005
  • Forouzandeh S., Sheikhahmadi A., Rezaei Aghdam A., Xu S., 2018. New centrality measure for nodes based on user social status and behavior on Facebook. International Journal of Web Information Systems, 14(2), pp.158-176.DOI: 10.1108/IJWIS-07-2017-0053
  • Agryzkov T., Tortosa L., Vicent J., 2016. New highlights and a new centrality measure based on the Adapted PageRank Algorithm for urban networks. Applied Mathematics and Computation, 291, pp.14-29.DOI: 10.1016/j.amc.2016.06.036
  • Syarif A., Abouaissa A., Idoumghar L., Lorenz P., Schott R., Staples G., 2019. New path centrality based on operator calculus approach for wireless sensor network deployment. IEEE Transactions on Emerging Topics in Computing, 7(1), pp.162-173.DOI: 10.1109/TETC.2016.2585045
  • Punithavelan, N. and Jaganathan, B., 2017. New web page rank method using HITS Centrality. Global Journal of Pure and Applied Mathematics, 13(10), pp.7229-7235.
  • Lyu T., Sun F., Zhang Y., 2020. Node Conductance: A Scalable Node Centrality Measure on Big Networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12085 LNAI, pp.529-541.DOI: 10.1007/978-3-030-47436-2_40
  • Amano S., Ogawa K., Miyake Y., 2018. Node property of weighted networks considering connectability to nodes within two degrees of separation. Scientific Reports, 8(1).DOI: 10.1038/s41598-018-26781-y
  • Simko G., Csermely P., 2013. Nodes Having a Major Influence to Break Cooperation Define a Novel Centrality Measure: Game Centrality. PLoS ONE, 8(6).DOI: 10.1371/journal.pone.0067159
  • Arrigo F., Grindrod P., Higham D., Noferini V., 2018. Non-backtracking walk centrality for directed networks. Journal of Complex Networks, 6(1), pp.54-78.DOI: 10.1093/comnet/cnx025
  • Ranjan, G. and Zhang, Z.L., 2010. On random eccentricity in complex networks. Tech. Report.
  • Criado R., Flores J., García E., del Amo A.J.G., Pérez Á., Romance M., 2019. On the α-nonbacktracking centrality for complex networks: Existence and limit cases. Journal of Computational and Applied Mathematics, 350, pp.35-45.DOI: 10.1016/j.cam.2018.09.048
  • Reiffers-Masson A., Labatut V., 2017. Opinion-based centrality in multiplex networks: A convex optimization approach. Network Science, 5(2), pp.213-234.DOI: 10.1017/nws.2017.7
  • Andrade R., Rêgo L., 2019. p-means centrality. Communications in Nonlinear Science and Numerical Simulation, 68, pp.41-55.DOI: 10.1016/j.cnsns.2018.08.002
  • Chua H., Bhowmick S., Tucker-Kellogg L., Zhao Q., Dewey C., Yu H., 2011. PANI: A novel algorithm for fast discovery of Putative TArget Nodes in signaling networks. 2011 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2011, , pp.284-288.DOI: 10.1145/2147805.2147836
  • Ghosh R., Lerman K., 2011. Parameterized centrality metric for network analysis. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 83(6).DOI: 10.1103/PhysRevE.83.066118
  • Senturk I.F., 2019. Partition-aware centrality measures for connectivity restoration in mobile sensor networks. International Journal of Sensor Networks, 30(1), pp.1-12.DOI: 10.1504/IJSNET.2019.099218
  • Park J., Hescott B.J., Slonim D.K., 2019. Pathway centrality in protein interaction networks identifies putative functional mediating pathways in pulmonary disease. Scientific Reports, 9(1).DOI: 10.1038/s41598-019-42299-3
  • Piraveenan M., Prokopenko M., Hossain L., 2013. Percolation Centrality: Quantifying Graph-Theoretic Impact of Nodes during Percolation in Networks. PLoS ONE, 8(1).DOI: 10.1371/journal.pone.0053095
  • Szalay K., Csermely P., 2013. Perturbation Centrality and Turbine: A Novel Centrality Measure Obtained Using a Versatile Network Dynamics Tool. PLoS ONE, 8(10).DOI: 10.1371/journal.pone.0078059
  • Bonacich, P., 1987. Power and centrality: A family of measures. American journal of sociology, 92(5), pp.1170-1182.DOI: 10.1086/228631
  • Smith J., Halgin D., Kidwell-Lopez V., Labianca G., Brass D., Borgatti S., 2014. Power in politically charged networks. Social Networks, 36(1), pp.162-176.DOI: 10.1016/j.socnet.2013.04.007
  • Tang X., Wang J., Zhong J., Pan Y., 2014. Predicting essential proteins basedon weighted degree centrality. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 11(2), pp.407-418.DOI: 10.1109/TCBB.2013.2295318
  • Luo J., Zhang N., 2014. Prediction of Essential Proteins Based On Edge Clustering Coefficient and Gene Ontology Information. Journal of Biological Systems, 22(3), pp.339-351.DOI: 10.1142/S0218339014500119
  • Qi, Y. and Luo, J., 2015. Prediction of essential proteins based on local interaction density. IEEE/ACM transactions on computational biology and bioinformatics, 13(6), pp.1170-1182.DOI: 10.1109/TCBB.2015.2509989
  • Hellervik A., Nilsson L., Andersson C., 2019. Preferential centrality – A new measure unifying urban activity, attraction and accessibility. Environment and Planning B: Urban Analytics and City Science, 46(7), pp.1331-1346.DOI: 10.1177/2399808318812888
  • Khadangi E., Bagheri A., 2017. Presenting novel application-based centrality measures for finding important users based on their activities and social behavior. Computers in Human Behavior, 73, pp.64-79.DOI: 10.1016/j.chb.2017.03.014
  • Izaac J.A., Wang J.B., Abbott P.C., Ma X.S., 2017. Quantum centrality testing on directed graphs via PT-symmetric quantum walks. Physical Review A, 96(3).DOI: 10.1103/PhysRevA.96.032305
  • Ma Y., Cao Z., Qi X., 2019. Quasi-Laplacian centrality: A new vertex centrality measurement based on Quasi-Laplacian energy of networks. Physica A: Statistical Mechanics and its Applications, 527.DOI: 10.1016/j.physa.2019.121130
  • Avrachenkov K., Borkar V., Nemirovsky D., 2010. Quasi-stationary distributions as centrality measures for the giant strongly connected component of a reducible graph. Journal of Computational and Applied Mathematics, 234(11), pp.3075-3090.DOI: 10.1016/j.cam.2010.02.001
  • Plana F., Perez J., 2019. QuickCent: A Fast and Frugal Heuristic for Centrality Estimation on Networks. Proceedings - 2018 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2018, (), pp.238-245.DOI: 10.1109/WI.2018.00-84
  • Wąs, T., Rahwan, T. and Skibski, O., 2019, July. Random Walk Decay Centrality. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 01, pp. 2197-2204).DOI: 10.1609/aaai.v33i01.33012197
  • Noh J., Rieger H., 2004. Random Walks on Complex Networks. Physical Review Letters, 92(11).DOI: 10.1103/PhysRevLett.92.118701
  • Ercsey-Ravasz M., Lichtenwalter R.N., Chawla N.V., Toroczkai Z., 2012. Range-limited centrality measures in complex networks. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 85(6).DOI: 10.1103/PhysRevE.85.066103
  • Negahban S., Oh S., Shah D., 2017. Rank centrality: Ranking from pairwise comparisons. Operations Research, 65(1), pp.266-287.DOI: 10.1287/opre.2016.1534
  • Kaur M., Singh S., 2017. Ranking based comparative analysis of graph centrality measures to detect negative nodes in online social networks. Journal of Computational Science, 23, pp.91-108.DOI: 10.1016/j.jocs.2017.10.018
  • De Domenico M., Solé-Ribalta A., Omodei E., Gómez S., Arenas A., 2015. Ranking in interconnected multilayer networks reveals versatile nodes. Nature Communications, 6.DOI: 10.1038/ncomms7868
  • Koschützki D., Schwöbbermeyer H., Schreiber F., 2007. Ranking of network elements based on functional substructures. Journal of Theoretical Biology, 248(3), pp.471-479.DOI: 10.1016/j.jtbi.2007.05.038
  • Wang Z., Pei X., Wang Y., Yao Y., 2017. Ranking the key nodes with temporal degree deviation centrality on complex networks. Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017, , pp.1484-1489.DOI: 10.1109/CCDC.2017.7978752
  • Lu P., Dong C., 2019. Ranking the spreading influence of nodes in complex networks based on mixing degree centrality and local structure. International Journal of Modern Physics B, 33(32).DOI: 10.1142/S0217979219503958
  • Yang F., Li X., Xu Y., Liu X., Wang J., Zhang Y., Zhang R., Yao Y., 2018. Ranking the spreading influence of nodes in complex networks: An extended weighted degree centrality based on a remaining minimum degree decomposition. Physics Letters, Section A: General, Atomic and Solid State Physics, 382(34), pp.2361-2371.DOI: 10.1016/j.physleta.2018.05.032
  • Cauteruccio, F., Terracina, G., Ursino, D. and Virgili, L., 2019. Redefining Betweenness Centrality in a Multiple IoT Scenario. In AI&IoT@ AI* IA (pp. 16-27).
  • Sotoodeh H., Falahrad M., 2019. Relative Degree Structural Hole Centrality, CRD−SH: A New Centrality Measure in Complex Networks. Journal of Systems Science and Complexity, 32(5), pp.1306-1323.DOI: 10.1007/s11424-018-7331-5
  • Vukičević, D., Škrekovski, R. and Tepeh, A., 2016. Relative edge betweenness centrality. Ars Mathematica Contemporanea, 12(2), pp.261-270.DOI: 10.26493/1855-3974.863.169
  • Dangalchev C., 2006. Residual closeness in networks. Physica A: Statistical Mechanics and its Applications, 365(2), pp.556-564.DOI: 10.1016/j.physa.2005.12.020
  • Del Sol A., Fujihashi H., Amoros D., Nussinov R., 2006. Residues crucial for maintaining short paths in network communication mediate signaling in proteins. Molecular Systems Biology, 2.DOI: 10.1038/msb4100063
  • Zhang, Y., Shao, C., He, S. and Gao, J., 2020. Resilience centrality in complex networks. Physical Review E, 101(2), p.022304.DOI: 10.1103/PhysRevE.101.022304
  • Naderi Yeganeh P., Naderi Yeganeh P., Richardson C., Saule E., Loraine A., Taghi Mostafavi M., 2020. Revisiting the use of graph centrality models in biological pathway analysis. BioData Mining, 13(1).DOI: 10.1186/s13040-020-00214-x
  • Lempel R., Moran S., 2002. SALSA: The stochastic approach for link-structure analysis. ACM Transactions on Information Systems, 19(2), pp.131-160.DOI: 10.1145/382979.383041
  • Zhang, W., 2016. Screening node attributes that significantly influence node centrality in the network. Selforganizology, 3(3), pp.75-86.
  • Kermarrec A.M., Le Merrer E., Sericola B., Trédan G., 2011. Second order centrality: Distributed assessment of nodes criticity in complex networks. Computer Communications, 34(5), pp.619-628.DOI: 10.1016/j.comcom.2010.06.007
  • Ni C., Yang J., Kong D., 2020. Sequential seeding strategy for social influence diffusion with improved entropy-based centrality. Physica A: Statistical Mechanics and its Applications, 545.DOI: 10.1016/j.physa.2019.123659
  • Jackson, M. O. 2008. Social and economic networks, volume 3. Princeton university press.
  • Li B., Gao Z., Shan X., Zhou W., Ferrara E., 2019. Sorec: A social-relation based centrality measure in mobile social networks. 2019 26th International Conference on Telecommunications, ICT 2019, , pp.485-489.DOI: 10.1109/ICT.2019.8798844
  • Liu A., Porter M.A., 2020. Spatial strength centrality and the effect of spatial embeddings on network architecture. Physical Review E, 101(6).DOI: 10.1103/PhysRevE.101.062305
  • Maslov S., Sneppen K., 2002. Specificity and stability in topology of protein networks. Science, 296(5569), pp.910-913.DOI: 10.1126/science.1065103
  • Estrada E., Rodríguez-Velázquez J.A., 2005. Spectral measures of bipartivity in complex networks. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 72(4).DOI: 10.1103/PhysRevE.72.046105
  • Carrizosa E., Marin A., Pelegrin M., 2020. Spotting Key Members in Networks: Clustering-Embedded Eigenvector Centrality. IEEE Systems Journal, 14(3), pp.3916-3925.DOI: 10.1109/JSYST.2020.2982266
  • Burt R., 2004. Structural holes and good ideas. American Journal of Sociology, 110(2), pp.349-399.DOI: 10.1086/421787
  • Estrada E., Rodríguez-Velázquez J.A., 2005. Subgraph centrality in complex networks. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 71(5).DOI: 10.1103/PhysRevE.71.056103
  • Saito K., Kimura M., Ohara K., Motoda H., 2016. Super mediator - A new centrality measure of node importance for information diffusion over social network. Information Sciences, 329, pp.985-1000.DOI: 10.1016/j.ins.2015.03.034
  • Madotto A., Liu J., 2016. Super-Spreader Identification Using Meta-Centrality. Scientific Reports, 6.DOI: 10.1038/srep38994
  • Béres F., Pálovics R., Oláh A., Benczúr A.A., 2018. Temporal walk based centrality metric for graph streams. Applied Network Science, 3(1).DOI: 10.1007/s41109-018-0080-5
  • Barrat A., Barthélemy M., Pastor-Satorras R., Vespignani A., 2004. The architecture of complex weighted networks. Proceedings of the National Academy of Sciences of the United States of America, 101(11), pp.3747-3752.DOI: 10.1073/pnas.0400087101
  • Glattfelder J., 2019. THE BOW-TIE CENTRALITY: A NOVEL MEASURE for DIRECTED and WEIGHTED NETWORKS with AN INTRINSIC NODE PROPERTY. Advances in Complex Systems, .DOI: 10.1142/S0219525919500188
  • Everett M.G., Borgatti S.P., 1999. The centrality of groups and classes. Journal of Mathematical Sociology, 23(3), pp.181-201.DOI: 10.1080/0022250X.1999.9990219
  • Kamvar S., Schlosser M., Garcia-Molina H., 2003. The EigenTrust algorithm for reputation management in P2P networks. Proceedings of the 12th International Conference on World Wide Web, WWW 2003, , pp.640-651.DOI: 10.1145/775152.775242
  • Ma X., Ma Y., 2019. The Local Triangle Structure Centrality Method to Rank Nodes in Networks. Complexity, 2019.DOI: 10.1155/2019/9057194
  • Potapov A., Goemann B., Wingender E., 2008. The pairwise disconnectivity index as a new metric for the topological analysis of regulatory networks. BMC Bioinformatics, 9.DOI: 10.1186/1471-2105-9-227
  • De Medeiros D.S.V., Campista M.E.M., Mitton N., De Amorim M.D., Pujolle G., 2017. The Power of Quasi-Shortest Paths: ρ-Geodesic Betweenness Centrality. IEEE Transactions on Network Science and Engineering, 4(3), pp.187-200.DOI: 10.1109/TNSE.2017.2708705
  • Alshahrani M., Fuxi Z., Sameh A., Mekouar S., Huang S., 2018. Top-K influential users selection based on combined Katz centrality and propagation probability. 2018 3rd IEEE International Conference on Cloud Computing and Big Data Analysis, ICCCBDA 2018, , pp.52-56.DOI: 10.1109/ICCCBDA.2018.8386486
  • Zaoli S., Mazzarisi P., Lillo F., 2019. Trip Centrality: walking on a temporal multiplex with non-instantaneous link travel time. Scientific Reports, 9(1).DOI: 10.1038/s41598-019-47115-6
  • Richters O., Peixoto T., 2011. Trust transitivity in social networks. PLoS ONE, 6(4).DOI: 10.1371/journal.pone.0018384
  • Pu C., Cui W., Yang J., 2014. Tunable path centrality: Quantifying the importance of paths in networks. Physica A: Statistical Mechanics and its Applications, 405, pp.267-277.DOI: 10.1016/j.physa.2014.03.039
  • Weng J., Lim E.P., Jiang J., He Q., 2010. TwitterRank: Finding topic-sensitive influential twitterers. WSDM 2010 - Proceedings of the 3rd ACM International Conference on Web Search and Data Mining, , pp.261-270.DOI: 10.1145/1718487.1718520
  • Lawyer G., 2015. Understanding the influence of all nodes in a network. Scientific Reports, 5.DOI: 10.1038/srep08665
  • Li, M., Lu, Y., Niu, Z. and Wu, F.X., 2017. United Complex Centrality for Identification of Essential Proteins from PPI Networks. IEEE/ACM transactions on computational biology and bioinformatics, 14(2), pp.370-380.DOI: 10.1109/TCBB.2015.2394487
  • Li G., Li M., Wang J., Li Y., Pan Y., 2020. United Neighborhood Closeness Centrality and Orthology for Predicting Essential Proteins. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(4), pp.1451-1458.DOI: 10.1109/TCBB.2018.2889978
  • Goh K., Kahng B., Kim D., 2001. Universal Behavior of Load Distribution in Scale-Free Networks. Physical Review Letters, 87(27), pp.278701-278701-4.DOI: 10.1103/PhysRevLett.87.278701
  • Nie T., Guo Z., Zhao K., Lu Z., 2016. Using mapping entropy to identify node centrality in complex networks. Physica A: Statistical Mechanics and its Applications, 453, pp.290-297.DOI: 10.1016/j.physa.2016.02.009
  • Gao L., Yu S., Li M., Shen Z., Gao Z., 2019. Weighted h-index for identifying influential spreaders. Symmetry, 11(10).DOI: 10.3390/sym11101263