Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Templates for the solution of algebraic eigenvalue problems: a practical guide
Templates for the solution of algebraic eigenvalue problems: a practical guide
Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Combining Multiple Weak Clusterings
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Natural communities in large linked networks
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Solving cluster ensemble problems by bipartite graph partitioning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Dynamic social network analysis using latent space models
ACM SIGKDD Explorations Newsletter
Proceedings of the 15th international conference on World Wide Web
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Ontologies are us: A unified model of social networks and semantics
Web Semantics: Science, Services and Agents on the World Wide Web
Intelligent Data Analysis
Spectral clustering and transductive learning with multiple views
Proceedings of the 24th international conference on Machine learning
An event-based framework for characterizing the evolutionary behavior of interaction graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A tutorial on spectral clustering
Statistics and Computing
Seeking stable clusters in the blogosphere
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Visual analysis of dynamic group membership in temporal social networks
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Learning multiple graphs for document recommendations
Proceedings of the 17th international conference on World Wide Web
Facetnet: a framework for analyzing communities and their evolutions in dynamic networks
Proceedings of the 17th international conference on World Wide Web
Community evolution in dynamic multi-mode networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Towards Semantic Social Networks
ESWC '07 Proceedings of the 4th European conference on The Semantic Web: Research and Applications
Integrating Folksonomies with the Semantic Web
ESWC '07 Proceedings of the 4th European conference on The Semantic Web: Research and Applications
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Mixed Membership Stochastic Blockmodels
The Journal of Machine Learning Research
Analyzing communities and their evolutions in dynamic social networks
ACM Transactions on Knowledge Discovery from Data (TKDD)
Analysis of community structure in Wikipedia
Proceedings of the 18th international conference on World wide web
Multi-view clustering via canonical correlation analysis
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Relational learning via latent social dimensions
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning systems of concepts with an infinite relational model
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Scalable learning of collective behavior based on sparse social dimensions
Proceedings of the 18th ACM conference on Information and knowledge management
Clustering with Multiple Graphs
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Uncoverning Groups via Heterogeneous Interaction Analysis
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Empirical comparison of algorithms for network community detection
Proceedings of the 19th international conference on World wide web
Multilevel algorithms for partitioning power-law graphs
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Chromatic correlation clustering
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Producing a unified graph representation from multiple social network views
Proceedings of the 5th Annual ACM Web Science Conference
Flexible and robust co-regularized multi-domain graph clustering
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Community detection by popularity based models for authored networked data
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
The community structure of a multidimensional network of news clips
International Journal of Web Based Communities
Learning latent representations of nodes for classifying in heterogeneous social networks
Proceedings of the 7th ACM international conference on Web search and data mining
Interesting pattern mining in multi-relational data
Data Mining and Knowledge Discovery
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The pervasiveness of Web 2.0 and social networking sites has enabled people to interact with each other easily through various social media. For instance, popular sites like Del.icio.us, Flickr, and YouTube allow users to comment on shared content (bookmarks, photos, videos), and users can tag their favorite content. Users can also connect with one another, and subscribe to or become a fan or a follower of others. These diverse activities result in a multi-dimensional network among actors, forming group structures with group members sharing similar interests or affiliations. This work systematically addresses two challenges. First, it is challenging to effectively integrate interactions over multiple dimensions to discover hidden community structures shared by heterogeneous interactions. We show that representative community detection methods for single-dimensional networks can be presented in a unified view. Based on this unified view, we present and analyze four possible integration strategies to extend community detection from single-dimensional to multi-dimensional networks. In particular, we propose a novel integration scheme based on structural features. Another challenge is the evaluation of different methods without ground truth information about community membership. We employ a novel cross-dimension network validation (CDNV) procedure to compare the performance of different methods. We use synthetic data to deepen our understanding, and real-world data to compare integration strategies as well as baseline methods in a large scale. We study further the computational time of different methods, normalization effect during integration, sensitivity to related parameters, and alternative community detection methods for integration.