Automatic detection of cohesive subgroups within social hypertext: A heuristic approach
The New Review of Hypermedia and Multimedia
Identifying and evaluating community structure in complex networks
Pattern Recognition Letters
A topical link model for community discovery in textual interaction graph
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Using cohesive subgroups for analyzing the evolution of the friend view mobile social network
UIC'10 Proceedings of the 7th international conference on Ubiquitous intelligence and computing
Community discovery using nonnegative matrix factorization
Data Mining and Knowledge Discovery
Mining topics on participations for community discovery
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Detection of communities and bridges in weighted networks
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Subgraph mining on directed and weighted graphs
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
V-SMART-join: a scalable mapreduce framework for all-pair similarity joins of multisets and vectors
Proceedings of the VLDB Endowment
Latent Community Topic Analysis: Integration of Community Discovery with Topic Modeling
ACM Transactions on Intelligent Systems and Technology (TIST)
DEMON: a local-first discovery method for overlapping communities
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
On spectral partitioning of co-authorship networks
CISIM'12 Proceedings of the 11th IFIP TC 8 international conference on Computer Information Systems and Industrial Management
A spatial LDA model for discovering regional communities
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Spectral graph multisection through orthogonality
Proceedings of the 4th MultiClust Workshop on Multiple Clusterings, Multi-view Data, and Multi-source Knowledge-driven Clustering
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Automatic discovery of community structures in complex networks is a fundamental task in many disciplines, including social science, engineering, and biology. Recently, a quantitative measure called modularity (Q) has been proposed to effectively assess the quality of community structures. Several community discovery algorithms have since been developed based on the optimization of Q. However, this optimization problem is NP-hard, and the existing algorithms have a low accuracy or are computationally expensive. In this paper, we present an efficient spectral algorithm for modularity optimization. When tested on a large number of synthetic or real-world networks, and compared to the existing algorithms, our method is efficient and and has a high accuracy. In addition, we have successfully applied our algorithm to detect interesting and meaningful community structures from real-world networks in different domains, including biology, medicine and social science. Due to space limitation, results of these applications are presented in a complete version of the paper available on our website (http://cse.wustl.edu/~jruan/).