A joint optimization framework for better community detection based on link prediction in social networks
Journal
Knowledge and Information Systems
Journal Volume
62
Journal Issue
11
Pages
4277-4296
Date Issued
2020
Author(s)
Abstract
Real-world network data can be incomplete (e.g., the social connections are partially observed) due to reasons such as graph sampling and privacy concerns. Consequently, communities detected based on such incomplete network information could be not as reliable as the ones identified based on the fully observed network. While existing studies first predict missing links and then detect communities, in this paper, a joint optimization framework, Communities detected on Predicted Edges, is proposed. Our goal to improve the quality of community detection through learning the probability of unseen links and the probability of community affiliation of nodes simultaneously. Link prediction and community detection are mutually reinforced to generate better results of both tasks. Experiments conducted on a number of well-known network data show that the proposed COPE stably outperforms several state-of-the-art community detection algorithms.
SDGs
Type
journal article