Work

A Centrality Maximization Approach for Link Recommendation

Year: 2025

Type: article

Abstract: In social networks, the goal of link recommendation is to recommend links for nodes and add them to the network, thereby satisfying the potential link interests of the nodes. The centrality of nodes in social networks typically quantifies the importance of nodes in the network. Some nodes may desire to increase their centrality by adding links. First, a multi-community centrality measurement method is proposed, and based on harmonic centrality, a hybrid centrality measurement method is introduced. Next, the link recommendation problem is regarded as a problem of maximizing node hybrid centrality, which can be formally modeled as a submodular function maximization problem. A greedy algorithm with performance guarantees can be directly applied to select the best links. Compared to existing link prediction and link recommendation algorithms, our algorithm recommends links that better improve the hybrid centrality of users.

Source: International Journal of Computer and Information Technology(2279-0764)

DOI: 10.24203/3712t522

Authors: Qi Zhang, Hao Zhong

Institutions: Guangzhou College of Commerce, South China Normal University

Language: English

Topic: Complex Network Analysis Techniques

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: diamond

Funders: National Natural Science Foundation of China, South China Normal University, National Key Research and Development Program of China

Awards: G2134346418, G7320323882, G8770775930