Community Detection and Link Prediction via Cluster-Driven Low-Rank Matrix Completion
Abstract
Community detection and link prediction are highly dependent since knowing cluster structure as a priori will help identify missing links, and in return, clustering on networks with supplemented missing links will improve community detection performance. In this paper, we propose a Cluster-driven Low-rank Matrix Completion (CLMC), for performing community detection and link prediction simultaneously in a unified framework. To this end, CLMC decomposes the adjacent matrix of a target network as three additive matrices: clustering matrix, noise matrix and supplement matrix. The community-structure and low-rank constraints are imposed on the clustering matrix, such that the noisy edges between communities are removed and the resulting matrix is an ideal block-diagonal matrix. Missing edges are further learned via low-rank matrix completion. Extensive experiments show that CLMC achieves state-of-the-art performance.
Cite
Text
Shao et al. "Community Detection and Link Prediction via Cluster-Driven Low-Rank Matrix Completion." International Joint Conference on Artificial Intelligence, 2019. doi:10.24963/IJCAI.2019/469Markdown
[Shao et al. "Community Detection and Link Prediction via Cluster-Driven Low-Rank Matrix Completion." International Joint Conference on Artificial Intelligence, 2019.](https://mlanthology.org/ijcai/2019/shao2019ijcai-community/) doi:10.24963/IJCAI.2019/469BibTeX
@inproceedings{shao2019ijcai-community,
title = {{Community Detection and Link Prediction via Cluster-Driven Low-Rank Matrix Completion}},
author = {Shao, Junming and Zhang, Zhong and Yu, Zhongjing and Wang, Jun and Zhao, Yi and Yang, Qinli},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2019},
pages = {3382-3388},
doi = {10.24963/IJCAI.2019/469},
url = {https://mlanthology.org/ijcai/2019/shao2019ijcai-community/}
}