# ME - Mapping Entropy Centrality

#### Definition

This concept is established according to the local information which considers the correlation among all neighbors of a node. This method identifies the importance of a node in the complex network.

Mapping entropy (ME) defines by applying the degree centralities of node $v_i$ and $v_j$.

$$ME_i=-DC_i {\underset{j=1}{\overset{M}{\sum}}} logDC_j$$

where $DC_i$ is the degree centrality of node $v_i$, and $DC_j$ is the degree centrality of one of its neighbor nodes, and $M$ is the neighbor set of node $v_i$.

The value of ME reflects the correlation between a node and its neighbor nodes. This is a new centrality definition based on knowledge of the neighbors of a node. The efficiency of the mapping entropy centrality is indicated from both the static style and the dynamic style.

Mapping entropy (ME) defines by applying the degree centralities of node $v_i$ and $v_j$.

$$ME_i=-DC_i {\underset{j=1}{\overset{M}{\sum}}} logDC_j$$

where $DC_i$ is the degree centrality of node $v_i$, and $DC_j$ is the degree centrality of one of its neighbor nodes, and $M$ is the neighbor set of node $v_i$.

The value of ME reflects the correlation between a node and its neighbor nodes. This is a new centrality definition based on knowledge of the neighbors of a node. The efficiency of the mapping entropy centrality is indicated from both the static style and the dynamic style.

#### References

- 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