LabelRank: A Stabilized Label Propagation Algorithm for Community Detection in Networks
arXiv:1303.0868
Abstract
An important challenge in big data analysis nowadays is detection of cohesive groups in large-scale networks, including social networks, genetic networks, communication networks and so. In this paper, we propose LabelRank, an efficient algorithm detecting communities through label propagation. A set of operators is introduced to control and stabilize the propagation dynamics. These operations resolve the randomness issue in traditional label propagation algorithms (LPA), stabilizing the discovered communities in all runs of the same network. Tests on real-world networks demonstrate that LabelRank significantly improves the quality of detected communities compared to LPA, as well as other popular algorithms.
Proc. IEEE Network Science Workshop, 2013
References in corpus (9)
- Fast unfolding of communities in large networks
- Finding community structure in networks using the eigenvectors of matrices
- Cooperative Game Theory Approaches for Network Partitioning
- Near linear time algorithm to detect community structures in large-scale networks
- Towards real-time community detection in large networks
- Finding Community Structure in Mega-scale Social Networks
- Community Detection Using A Neighborhood Strength Driven Label Propagation Algorithm
- SLPA: Uncovering Overlapping Communities in Social Networks via A Speaker-listener Interaction Dynamic Process
- Towards Linear Time Overlapping Community Detection in Social Networks
Cited by in corpus (4)
- OLCPM: An Online Framework for Detecting Overlapping Communities in Dynamic Social Networks
- LabelRankT: Incremental Community Detection in Dynamic Networks via Label Propagation
- DHLP 1&2: Giraph based distributed label propagation algorithms on heterogeneous drug-related networks
- Overlapping Communities in Complex Networks