Partitioning sparse matrices with eigenvectors of graphs
SIAM Journal on Matrix Analysis and Applications
Evaluate Nodes Importance in the Network Using Data Field Theory
ICCIT '07 Proceedings of the 2007 International Conference on Convergence Information Technology
Statistical properties of community structure in large social and information networks
Proceedings of the 17th international conference on World Wide Web
An Algorithm to Find Overlapping Community Structure in Networks
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Overlapping community structure detection in networks
Proceedings of the 17th ACM conference on Information and knowledge management
On finding graph clusterings with maximum modularity
WG'07 Proceedings of the 33rd international conference on Graph-theoretic concepts in computer science
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There has been considerable interest in designing algorithms for detecting community structure in real-world complex networks. A majority of these algorithms assume that communities are disjoint, placing each vertex in only one cluster. However, in nature, it is a matter of common experience that communities often overlap and members often play multiple roles in a network topology. To further investigate these properties of overlapping communities and heterogeneity within the network topology, a new method is proposed to divide networks into separate communities by spreading outward from each local important element and extracting its neighbors within the same group in each spreading operation. When compared with the state of the art, our new algorithm can not only classify different types of nodes at a more fine-grained scale successfully but also detect community structure more effectively. We also evaluate our algorithm using the standard data sets. Our results show that it performed well not only in the efficiency of algorithm, but also with a higher accuracy of partition results.