Towards Linear Time Overlapping Community Detection in Social Networks
By Jierui Xie, Boleslaw K. Szymanski. Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2012
298 citations recorded by the source corpus.
Membership diversity is a characteristic aspect of social networks in which a person may belong to more than one social group. For this reason, discovering overlapping structures is necessary for realistic social analysis. In this paper, we present a fast algorithm, called SLPA, for overlapping community detection in large-scale networks. SLPA spreads labels according to dynamic interaction rules. It can be applied to both unipartite and bipartite networks. It is also able to uncover overlapping nested hierarchy . The time complexity of SLPA scales linearly with the number of edges in the network. Experiments in both synthetic and real-world networks show that SLPA has an excellent performance in identifying both node and community level overlapping structures.
Subfields: Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/40aa83472207092f7cb40edc937490cb3a17c8b8
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Darko Hric
- Santo Fortunato
- Jia Wu
- Chuan Zhou
- Philip S. Yu
- Boleslaw K. Szymanski
- Cécile Paris
- Surya Nepal
In Statistical and Nonlinear Physics
- Darko Hric
- Boleslaw K. Szymanski
- Jierui Xie
- Santo Fortunato
- Philip S. Yu
- Stephen Kelley
- Chuan Zhou
- Jia Wu
Citations in the corpus
- Overlapping community detection in networks: The state-of-the-art and comparative study (2011, background): “SLPA 13 [Xie et al. 2011; Xie and Szymanski 2012] is a general speaker-listener based information propagation process.”
- Community detection in networks: A user guide (2016, methodology): “A): Ganxis, a method based on label propagation (Xie and Szymanski, 2012) and LinkCommunities, a method based on grouping edges instead of vertices (Ahn et al., 2010) (Section IV.”
- A Comprehensive Survey on Community Detection With Deep Learning (2021, background): “1), platform sponsors promote products to targeted users in detected communities [23], [24].”