Directional node strength entropy centrality: Ranking influential nodes in complex networks

Abstract

Identifying influential spreaders within a network is an important research area. Existing centrality metrics have limitations of either performing well on certain networks, but being computationally demanding, or having lower resolution in ranking. Also, most of the earlier studies ignore the directional and weighted aspect of a(n) relationship/edge that we exploit in the present study. In the real world, the relationships and influences between entities are often not symmetric. For example, a charismatic individual may have a significant impact on a common citizen, while the reverse may not be true. We propose a new approach called Directional Node Strength Entropy (DNSE), a topology-based method to identify critical nodes in an undirected network that can maximize spreading influence. An important neighbor exerts more influence on a node than it exerts back to that neighbor if its own importance is less than the neighbor. Our premise is that the strengths of network edges (connections) are directional and this strength depends on the importance of the starting node. We assign potential weights to the edges and use the degree of a node as a proxy for its importance. Directional node entropy across the neighborhood is used to rank the nodes. We conducted an extensive evaluation on real-world networks from various domains. We compared the proposed DNSE method against similar topology-based methods using Kendall’s rank correlation, ranking uniqueness, ccdf, and spreading influence, utilizing the SIR model as the benchmark. Results show that the proposed DNSE demonstrates superior or at-par performance compared to the state-of-the-art.