Identifying Influential Nodes in Complex Networks Based on Local Neighbor Contribution

Abstract

The identification of influential nodes in complex networks has been widely used to suppress rumor dissemination and control the spread of epidemics and diseases. However, achieving high accuracy and comprehensiveness in node influence ranking is time-consuming, and there are issues in using different measures on the same subject. The identification of influential nodes is very important for the maintenance of the entire network because they determine the stability and integrity of the entire network, which has strong practical application value in real life. Accordingly, a method based on local neighbor contribution (LNC) is proposed. LNC combines the influence of the nodes themselves with the contribution of the nearest and the next nearest neighbor nodes, thus further quantifying node influence in complex networks. LNC is applicable to networks of various scales, and its time complexity is considerably low. We evaluate the performance of LNC through extensive simulation experiments on seven real-world networks and two synthetic networks. We employ the SIR model to examine the spreading efficiency of each node and compare LNC with degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, PageRank, Hyperlink-Induced Topic Search(HITS), ProfitLeader, Gravity and Weighted Formal Concept Analysis(WFCA). It is demonstrated that LNC ranks nodes effectively and outperforms several state-of-theart algorithms. INDEX TERMS Complex networks, influential nodes, local structure, neighbor contribution.

Identification of Influential Nodes via Effective Distance‐based Centrality Mechanism in Complex Networks

“…Two types of centrality measures can be used for identification of key nodes, i.e., (i) matrix-based centrality such as random walk matrix [32], Laplacian matrix [33], and adjacency matrix [34] (ii) superficial-based properties such as CC, BC, DC, and motif centrality measurement [35]. Currently, a vast number of studies have been offered regarding the identification of the key nodes, e.g., node removals, profit leader [36], and local neighbor contribution [3]; these studies have their own advantages and disadvantages. Zhong-Kui Bao et al [37] proposed heuristic clustering (HC) method of detecting the most influential nodes; HC works on the basis of similarity index which categorizes nodes into various clusters; in this way, the center nodes in clusters are taken as multiple spreaders.…”

“…In recent years, complex networks are an attractive and hot research area by virtue of its wide range of practical and theoretical applications in many major fields [1][2][3][4][5]. Several real-world application areas such as management science, chemistry, economics, and financial systems [6,7], computer science, biological science [8,9], and many other similar fields can be regarded as complex networks [10,11].…”

A Survey on Recent Methods of Finding Influential Nodes in Complex Networks

“…Dai et al [11] proposed the local neighbor contribution (LNC) method which combined the influence of the nodes with the contribution of the nearest and next-nearest neighbor nodes. Node contraction defines the influence of a node to be equivalent to the destructiveness of the network after the node is removed.…”

“…In recent years, the research on complex networks has gained attention in various fields such as social networks, collaboration networks, email network, biological science, brain networks, railway networks, climate networks, international trade networks and technological networks [5,6,7,8,9,11]. Understanding dynamics of information spreading processes in a complex networks is an important topic with many diverse applications, such as information dissemination, information propagation, viral marketing, controlling rumors and opinion monitoring.…”

Identification of Influential Nodes for Drone Swarm Based on Graph Neural Networks

“…The spreading power of the method is the strongest, but the method has a large amount of calculation with large time consumption. Jin et al [39] propose a method based on the local neighbor contribution (LNC). LNC combines the influence of the node itself with the contribution of the nearest neighbor and the second nearest neighbor.…”