Identifying Influencers in Social Networks

Abstract

Social network analysis is a multidisciplinary research covering informatics, mathematics, sociology, management, psychology, etc. In the last decade, the development of online social media has provided individuals with a fascinating platform of sharing knowledge and interests. The emergence of various social networks has greatly enriched our daily life, and simultaneously, it brings a challenging task to identify influencers among multiple social networks. The key problem lies in the various interactions among individuals and huge data scale. Aiming at solving the problem, this paper employs a general multilayer network model to represent the multiple social networks, and then proposes the node influence indicator merely based on the local neighboring information. Extensive experiments on 21 real-world datasets are conducted to verify the performance of the proposed method, which shows superiority to the competitors. It is of remarkable significance in revealing the evolutions in social networks and we hope this work will shed light for more and more forthcoming researchers to further explore the uncharted part of this promising field.

Cross-Platform Comparative Study of Public Concern on Social Media during the COVID-19 Pandemic: An Empirical Study Based on Twitter and Weibo

“…However, in the extant literature only a few scholars have performed a cross-national comparative analysis; most studies instead compare different media within a single country to determine factors such as partisanship or compare media in different countries in order to frame the same event or group. Scholarly studies that examine both social media in different societies and various forms of media within a given society are very scarce [ 54 , 55 ].…”

MINE: Identifying Top-k Vital Nodes in Complex Networks via Maximum Influential Neighbors Expansion

“…The research opens a new direction of understanding network structures, while the improvements in computational complexity are expected for further applications. INF [33] is a novel centrality measure that merely considers the local neighboring information of a focal node and can be further applied to multilayer networks. Extensive experiments on real-world datasets suggest its capability in identifying influencers in social networks.…”

“…We employ the INF indicator [33] to measure the initial leader of communities and then design a MINE algorithm to identify the vital nodes hierarchically. Suppose k is the number of nodes to be identified, the process of MINE is described as the following steps.…”

“…... ; // Load nodes V, edges E from G; # Step 1 for i in V do // Calculate the influence for each node i.group = null; // Initilize each node with no community label for j in i.neighbors do i.in f += γw ij /k j ; // γ = 1 for monolayer networks, while for multilayer networks, γ is determined by the edge type [33] I = sort(V, key=inf)↓; // Sorted V by node influence in descending order; # Step 2 for i in I do if i.group == null then i.group = i; // Mark the top influential nodes as the initial core of a community i.neighbors = sort(i.neighbors, key=inf)↓;…”

Social Influence Maximization in Hypergraphs

“…As a domino cascade, such influence impacts not only those people but also propagates through the social network, with varying degrees of effectiveness. This word-of-mouth propagation has been shown to be a powerful tool in different applications, such as viral marketing [ 2 , 3 ], recommendation [ 4 , 5 , 6 ], influential bloggers identification [ 7 , 8 , 9 ] and expert finding [ 10 , 11 ].…”