Key Node Discovery Algorithm Based on Multiple Relationships and Multiple Features in Social Networks

Xianyong Li,Yanjie Li,Yajun Du,Ying Tang,Zaoli Yang

doi:10.1155/2021/1956356

Abstract

The key nodes play important roles in the processes of information propagation and opinion evolution in social networks. Previous work rarely considered multiple relationships and features into key node discovery algorithms at the same time. Based on the relational networks including the forwarding network, replying network, and mentioning network in a social network, this paper first proposes an algorithm of the overlapping user relational network to extract different relational networks with same nodes. Integrated with these relational networks, a multirelationship network is established. Subsequently, a key node discovery (KND) algorithm is presented on the basis of the shortest path, degree centrality, and random walk features in the multirelationship network. The advantages of the proposed KND algorithm are proved by the SIR propagation model and the normalized discounted cumulative gain on the multirelationship networks and single-relation networks. The experiment’s results show that the proposed KND method for finding the key nodes is superior to other baseline methods on different networks.

Highlights

With the rapid development of social networks (e.g., Facebook, Twitter, and Sina Weibo), they have become main platforms for people to obtain, spread, and exchange information
Evaluation Experiments of key node discovery (KND) Algorithm. e SIR (Susceptible-Infected-Removed) model [9] is adopted to compare transmission ability on the KND algorithm and baseline methods. e susceptible population S is converted to the infected population with the probability η, and the infected population I recovers to be immune to the information with probability ξ
Suppose that NDCG@n denotes the normalized discounted cumulative gain of the first n nodes; DCGn represents the cumulative loss gain of the nodes; and IDCGn represents the maximum DCGn in the ideal case. en NDCG@n DCGn, IDCGn where DCGn 􏽐ni 1 Rl(i)/log(i + 1); and Rl(i) represents the correlation between node i and the final result

Summary

Introduction

With the rapid development of social networks (e.g., Facebook, Twitter, and Sina Weibo), they have become main platforms for people to obtain, spread, and exchange information. Chen et al [7] proposed a degree discount centrality algorithm to effectively make influence maximization. Eir experiments showed that the performance of this algorithm was better than other measures including high degree and betweenness in eight large-scale networks. Eir experiments showed that the performance of this algorithm is significantly superior to the degree centrality and k-core decomposition. There are many methods to find the key nodes in networks, such as feature vector method [11], shortest path increment [12], spreading influence related centrality [13], PageRank [14], LeaderRank [15], HITS [16], k-shell centrality [17], and k-shell improved algorithm [18,19,20,21]. It can be seen that the key node identification is very successful in single-relation networks

Methods

Results

Conclusion