Identifying Influential Nodes in Large-Scale Directed Networks: The Role of Clustering

Abstract

Identifying influential nodes in very large-scale directed networks is a big challenge relevant to disparate applications, such as accelerating information propagation, controlling rumors and diseases, designing search engines, and understanding hierarchical organization of social and biological networks. Known methods range from node centralities, such as degree, closeness and betweenness, to diffusion-based processes, like PageRank and LeaderRank. Some of these methods already take into account the influences of a node’s neighbors but do not directly make use of the interactions among it’s neighbors. Local clustering is known to have negative impacts on the information spreading. We further show empirically that it also plays a negative role in generating local connections. Inspired by these facts, we propose a local ranking algorithm named ClusterRank, which takes into account not only the number of neighbors and the neighbors’ influences, but also the clustering coefficient. Subject to the susceptible-infected-recovered (SIR) spreading model with constant infectivity, experimental results on two directed networks, a social network extracted from delicious.com and a large-scale short-message communication network, demonstrate that the ClusterRank outperforms some benchmark algorithms such as PageRank and LeaderRank. Furthermore, ClusterRank can also be applied to undirected networks where the superiority of ClusterRank is significant compared with degree centrality and k-core decomposition. In addition, ClusterRank, only making use of local information, is much more efficient than global methods: It takes only 191 seconds for a network with about nodes, more than 15 times faster than PageRank.

How to Identify the Most Powerful Node in Complex Networks? A Novel Entropy Centrality Approach

“…Considering the limitations of degree centrality and restriction of closeness centrality and betweenness centrality in large-scale networks, Chen et al [26] proposed a semi-local centrality method. Also, Chen et al [27] introduced a so-called ClusterRank method, which takes the influence of neighbor nodes and clustering coefficient into consideration. Zeng and Zhang [28] improved the established k-shell method by rethinking the significant connections between nodes and removed nodes and proposed a mixed degree decomposition method.…”

A Community-Based Approach to Identifying Influential Spreaders

“…As the link relationship between webpages can be explained as the correlation and support between webpages, so too can the importance of the webpage be judged. Typical methods include the Hypertext-Induced Topic Search (HITS) algorithm [25] proposed by Kleinberg, the PageRank algorithm [26] used by Google and LeaderRank [27] proposed recently by Lv Linyuan et al Then in 2014, Weighted LeaderRank [28] as an improvement method was presented by Li et al Current research on identifying influential spreaders, many interesting conclusions were successively put forward, such as the role of clustering [29] by Chen D-B et al who also proposed to improve the identification of influential spreaders by the path diversity [30].…”

Competition‐Based Benchmarking of Influence Ranking Methods in Social Networks

“…After a review of the most recent advances in complex network analysis, we find that the method of simply selecting the top spreaders from the entire network is consistently found throughout literature [35,37,38,[59][60][61][62]. Nevertheless, there are several alternatives for selecting multiple spreaders which we detail in the Supplementary Materials, Section 6.…”

“…Another method considered a local ranking measure is ClusterRank (CR), proposed by Chen et al [35]. CR quantifies the influence of a node v i by taking into account not only its direct influence (out-degree k out i ) and influences of its neighbours (like in the case of PageRank) but also its clustering coefficient c i [56].…”

“…The first category of scientists argues that the location of a node is more important than its immediate ego network and thus proposed k-core decomposition [28,29], along with improved variants, such as [30][31][32][33]. The second category of scientists quantifies the influence of a node based solely on its local surroundings [34][35][36]. Finally, the third category of scientists evaluates node influences according to various states of equilibrium for dynamical processes, such as random walks [37,38] or step-wise refinements [39].…”