Link Prediction and Graph Structure Estimation for Community Detection

Dongming Chen, Mingshuo Nie (), Fei Xie, Dongqi Wang and Huilin Chen
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Dongming Chen: Software College, Northeastern University, Shenyang 110819, China
Mingshuo Nie: Software College, Northeastern University, Shenyang 110819, China
Fei Xie: Software College, Northeastern University, Shenyang 110819, China
Dongqi Wang: Software College, Northeastern University, Shenyang 110819, China
Huilin Chen: College of Engineering, Computing and Cybernetics, Australian National University, Canberra, ACT 2601, Australia

Mathematics, 2024, vol. 12, issue 8, 1-16

Abstract: In real-world scenarios, obtaining the relationships between nodes is often challenging, resulting in incomplete network topology. This limitation significantly reduces the applicability of community detection methods, particularly neighborhood aggregation-based approaches, on structurally incomplete networks. Therefore, in this situation, it is crucial to obtain meaningful community information from the limited network structure. To address this challenge, the LPGSE algorithm was designed and implemented, which includes four parts: link prediction, structure observation, network estimation, and community partitioning. LPGSE demonstrated its performance in community detection in structurally incomplete networks with 10% missing edges on multiple datasets. Compared with traditional community detection algorithms, LPGSE achieved improvements in NMI and ARI metrics of 1.5781% to 29.0780% and 0.4332% to 31.9820%, respectively. Compared with similar community detection algorithms for structurally incomplete networks, LPGSE also outperformed other algorithms on all datasets. In addition, different edge-missing ratio settings were also attempted, and the performance of different algorithms in these situations was compared and analyzed. The results showed that the algorithm can still maintain high accuracy and stability in community detection across different edge-missing ratios.

Keywords: community detection; incomplete structure; edge-missing; link prediction; graph structure estimation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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