Paper Info
Title
Generalized network community detection
Abstract
Community structure is largely regarded as an intrinsic property of complex real-world networks. However, recent studies reveal that networks comprise even more sophisticated modules than classical cohesive communities. More precisely, real-world networks can also be naturally partitioned according to common patterns of connections between the nodes. Recently, a propagation based algorithm has been proposed for the detection of arbitrary network modules. We here advance the latter with a more adequate community modeling based on network clustering. The resulting algorithm is evaluated on various synthetic benchmark networks and random graphs. It is shown to be comparable to current state-of-the-art algorithms, however, in contrast to other approaches, it does not require some prior knowledge of the true community structure. To demonstrate its generality, we further employ the proposed algorithm for community detection in different unipartite and bipartite real-world networks, for generalized community detection and also predictive data clustering.
Year
Venue
Keywords
2011
CoRR
data clustering,data analysis,community structure,random graph,communication model
Field
DocType
Volume
Data mining,Intrinsic and extrinsic properties (philosophy),Network clustering,Community structure,Random graph,Bipartite graph,Artificial intelligence,Cluster analysis,Mathematics,Machine learning,Generality
Journal
abs/1110.2711
ISSN
Citations 
PageRank 
Proceedings of the ECML PKDD Workshop on Finding Patterns of Human Behaviors in Network and Mobility Data 2011 (NEMO '11), pp. 66-84
2
0.39
References 
Authors
10
2
Name
Order
Citations
PageRank
Lovro Subelj120916.37
Marko Bajec246534.56