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

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

Qin, Xi (South China University of Technology; Guangxi University)
Zhong, Cheng (Guangxi University)
Lin, H.X. (TU Delft Mathematical Physics)

2023

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.

community diffusion
feature learning
feature representation
Network embedding

http://resolver.tudelft.nl/uuid:7cc576a1-e637-4ac7-902d-2579072649b5

https://doi.org/10.1145/3594544

1556-4681

ACM Transactions on Knowledge Discovery from Data, 17 (8)

Institutional Repository

journal article

© 2023 Xi Qin, Cheng Zhong, H.X. Lin