Community-Based Influence Maximization Using Network Embedding in Dynamic Heterogeneous Social Networks

Xi Qin, Cheng Zhong*, Hai Xiang Lin

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
55 Downloads (Pure)

Abstract

Influence maximization (IM) is a very important issue in social network diffusion analysis. The topology of real social network is large-scale, dynamic, and heterogeneous. The heterogeneity, and continuous expansion and evolution of social network pose a challenge to find influential users. Existing IM algorithms usually assume that social networks are static or dynamic but homogeneous to simplify the complexity of the IM problem. We propose a community-based influence maximization algorithm using network embedding in dynamic heterogeneous social networks. We use DyHATR algorithm to obtain the propagation feature vectors of network nodes, and execute k-means cluster algorithm to transform the original network into a coarse granularity network (CGN). On CGN, we propose a community-based three-hop independent cascade model and construct the objective function of IM problem. We design a greedy heuristics algorithm to solve the IM problem with approximation guarantee and use community structure to quickly identify seed users and estimate their influence value. Experimental results on real social networks demonstrated that compared with existing IM algorithms, our proposed algorithm had better comprehensive performance with respect to the influence value, more less execution time and memory consumption, and better scalability.

Original languageEnglish
Article number119
JournalACM Transactions on Knowledge Discovery from Data
Volume17
Issue number8
DOIs
Publication statusPublished - 2023

Funding

This work was supported by the Special Project of Science and Technology Development Research of Guangxi under grant no. ZL19107008.

Keywords

  • community diffusion
  • feature learning
  • feature representation
  • Network embedding

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