Maximum Influence Degree

see "Diffusion Degree"

Diffusion degree considers neighbors’ contributions in addition to the degree of a node. The measure also works flawlessly with non uniform propagation probability distributions. On the other hand, Maximum Influence Degree provides the maximum theoretically possible influence (Upper Bound) for a node. We use Diffusion Degree Heuristic (DiDH) and Maximum Influence Degree Heuristic (MIDH), to find the top k influential individuals. k seeds obtained through these for both the setups show superior influence compared to the seeds obtained by high degree heuristics, degree discount heuristics, different variants of set covering greedy algorithms and Prefix excluding Maximum Influence Arborescence (PMIA) algorithm [PAL, S. K., 2014].

• KUNDU, S., MURTHY, C. A. & PAL, S. K. 2011. A New Centrality Measure for Influence Maximization in Social Networks. In: KUZNETSOV, S., MANDAL, D., KUNDU, M. & PAL, S. (eds.) Pattern Recognition and Machine Intelligence. Springer Berlin Heidelberg.
• PAL, S. K., KUNDU, S. & MURTHY, C. 2014. Centrality Measures, Upper Bound, and Influence Maximization in Large Scale Directed Social Networks. Fundamenta Informaticae, 130, 317-342.