Partitioning sparse matrices with eigenvectors of graphs
SIAM Journal on Matrix Analysis and Applications
Constructing good quality web page communities
ADC '02 Proceedings of the 13th Australasian database conference - Volume 5
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Conductance and congestion in power law graphs
SIGMETRICS '03 Proceedings of the 2003 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Utilizing hyperlink transitivity to improve web page clustering
ADC '03 Proceedings of the 14th Australasian database conference - Volume 17
Web Communities: Models and Algorithms
World Wide Web
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Proceedings of the 15th international conference on World Wide Web
Blocking Conductance and Mixing in Random Walks
Combinatorics, Probability and Computing
Mathematical aspects of mixing times in Markov chains
Foundations and Trends® in Theoretical Computer Science
Detecting overlapping community structures in networks with global partition and local expansion
APWeb'08 Proceedings of the 10th Asia-Pacific web conference on Progress in WWW research and development
Web communities identification from random walks
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Efficient identification of overlapping communities
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
Mining large query induced graphs towards a hierarchical query folksonomy
SPIRE'10 Proceedings of the 17th international conference on String processing and information retrieval
Identifying community structures in networks with seed expansion
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
Applying sentiment and social network analysis in user modeling
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Mining query log graphs towards a query folksonomy
Concurrency and Computation: Practice & Experience
A separability framework for analyzing community structure
ACM Transactions on Knowledge Discovery from Data (TKDD) - Casin special issue
Mesoscopic analysis of networks with genetic algorithms
World Wide Web
Exploiting small world property for network clustering
World Wide Web
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Community structure has been recognized as an important statistical feature of networked systems over the past decade. A lot of work has been done to discover isolated communities from a network, and the focus was on developing of algorithms with high quality and good performance. However, there is less work done on the discovery of overlapping community structure, even though it could better capture the nature of network in some real-world applications. For example, people are always provided with varying characteristics and interests, and are able to join very different communities in their social network. In this context, we present a novel overlapping community structures detecting algorithm which first finds the seed sets by the spectral partition and then extends them with a special random walks technique. At every expansion step, the modularity function Q is chosen to measure the expansion structures. The function has become one of the popular standards in community detecting and is defined in Newman and Girvan (Phys. Rev. 69:026113, 2004). We also give a theoretic analysis to the whole expansion process and prove that our algorithm gets the best community structures greedily. Extensive experiments are conducted in real-world networks with various sizes. The results show that overlapping is important to find the complete community structures and our method outperforms the C-means in quality.