Spectral K-way ratio-cut partitioning and clustering
DAC '93 Proceedings of the 30th international Design Automation Conference
A multilevel algorithm for partitioning graphs
Supercomputing '95 Proceedings of the 1995 ACM/IEEE conference on Supercomputing
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A generalized maximum entropy approach to bregman co-clustering and matrix approximation
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Co-clustering by block value decomposition
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Orthogonal nonnegative matrix t-factorizations for clustering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Unsupervised learning on k-partite graphs
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Combining content and link for classification using matrix factorization
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Clustering short texts using wikipedia
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A probabilistic framework for relational clustering
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for community identification in dynamic social networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning multiple graphs for document recommendations
Proceedings of the 17th international conference on World Wide Web
Binary Matrix Factorization with Applications
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Recommendation via Query Centered Random Walk on K-Partite Graph
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Scalable community discovery on textual data with relations
Proceedings of the 17th ACM conference on Information and knowledge management
Detecting Overlapping Community Structures in Networks
World Wide Web
MetaFac: community discovery via relational hypergraph factorization
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Combining link and content for community detection: a discriminative approach
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Robust label propagation on multiple networks
IEEE Transactions on Neural Networks
Clustering with Multiple Graphs
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Identifying Cohesive Subgroups and Their Correspondences in Multiple Related Networks
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
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Community detection in networks is an active area of research with many practical applications. However, most of the early work in this area has focused on partitioning a single network or a bipartite graph into clusters/communities. With the rapid proliferation of online social media, it has become increasingly common for web users to have noticeable presence across multiple web sites. This raises the question whether it is possible to combine information from several networks to improve community detection. In this paper, we present a framework that identifies communities simultaneously across different networks and learns the correspondences between them. The framework is applicable to networks generated from multiple web sites as well as to those derived from heterogeneous nodes of the same web site. It also allows the incorporation of prior information about the potential relationships between the communities in different networks. Extensive experiments have been performed on both synthetic and real-life data sets to evaluate the effectiveness of our framework. Our results show superior performance of simultaneous community detection over three alternative methods, including normalized cut and matrix factorization on a single network or a bipartite graph.