A Node Embedding-Based Influential Spreaders Identification Approach

Dongming Chen, Panpan Du, Bo Fang, Dongqi Wang and Xinyu Huang
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Dongming Chen: Software College, Northeastern University, Shenyang 110169, Liaoning, China
Panpan Du: Software College, Northeastern University, Shenyang 110169, Liaoning, China
Bo Fang: Software College, Northeastern University, Shenyang 110169, Liaoning, China
Dongqi Wang: Software College, Northeastern University, Shenyang 110169, Liaoning, China
Xinyu Huang: Software College, Northeastern University, Shenyang 110169, Liaoning, China

Mathematics, 2020, vol. 8, issue 9, 1-19

Abstract: Node embedding is a representation learning technique that maps network nodes into lower-dimensional vector space. Embedding nodes into vector space can benefit network analysis tasks, such as community detection, link prediction, and influential node identification, in both calculation and richer application scope. In this paper, we propose a two-step node embedding-based solution for the social influence maximization problem (IMP). The solution employs a revised network-embedding algorithm to map input nodes into vector space in the first step. In the second step, the solution clusters the vector space nodes into subgroups and chooses the subgroups’ centers to be the influential spreaders. The proposed approach is a simple but effective IMP solution because it takes both the social reinforcement and homophily characteristics of the social network into consideration in node embedding and seed spreaders selection operation separately. The information propagation simulation experiment of single-point contact susceptible-infected-recovered (SIR) and full-contact SIR models on six different types of real network data sets proved that the proposed social influence maximization (SIM) solution exhibits significant propagation capability.

Keywords: influence maximization; network embedding; weighted CBOW; clustering (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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