On a new multivariate two-sample test
Journal of Multivariate Analysis
Dynamic social network analysis using latent space models
ACM SIGKDD Explorations Newsletter
Evolutionary spectral clustering by incorporating temporal smoothness
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Statistical change detection for multi-dimensional data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
GraphScope: parameter-free mining of large time-evolving graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Facetnet: a framework for analyzing communities and their evolutions in dynamic networks
Proceedings of the 17th international conference on World Wide Web
Subspace Clustering Meets Dense Subgraph Mining: A Synthesis of Two Paradigms
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
DB-CSC: a density-based approach for subspace clustering in graphs with feature vectors
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
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Data sources representing social networks with additional attribute information about the nodes are widely available in today's applications. Recently, combined clustering methods were introduced that consider graph information and attribute information simultaneously to detect meaningful clusters in such networks. In many cases, such attributed graphs also evolve over time. Therefore, there is a need for clustering methods that are able to trace clusters over different time steps and analyze their evolution over time. In this paper, we extend our combined clustering method DB-CSC to the analysis of evolving combined clusters.