Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
On mining cross-graph quasi-cliques
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
CLAN: An Algorithm for Mining Closed Cliques from Large Dense Graph Databases
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Coherent closed quasi-clique discovery from large dense graph databases
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A spectral clustering approach to optimally combining numericalvectors with a modular network
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Effective Pruning Techniques for Mining Quasi-Cliques
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
ACM Transactions on Knowledge Discovery from Data (TKDD)
Discovering Relevant Cross-Graph Cliques in Dynamic Networks
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Managing and Mining Graph Data
Managing and Mining Graph Data
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
External evaluation measures for subspace clustering
Proceedings of the 20th ACM international conference on Information and knowledge management
RMiCS: a robust approach for mining coherent subgraphs in edge-labeled multi-layer graphs
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
Towards metric fusion on multi-view data: a cross-view based graph random walk approach
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Mining dense subgraphs such as cliques or quasi-cliques is an important graph mining problem and closely related to the notion of graph clustering. In various applications, graphs are enriched by additional information. For example, we can observe graphs representing different types of relations between the vertices. These multiple edge types can also be viewed as different "layers" of the same graph, which is denoted as a "multi-layer graph" in this work. Additionally, each edge might be annotated by a label characterizing the given relation in more detail. By exploiting all these different kinds of information, the detection of more interesting clusters in the graph can be supported. In this work, we introduce the multi-layer coherent subgraph (MLCS) model, which defines clusters of vertices that are densely connected by edges with similar labels in a subset of the graph layers. We avoid redundancy in the result by selecting only the most interesting, non-redundant clusters for the output. Based on this model, we introduce the best-first search algorithm MiMAG. In thorough experiments we demonstrate the strengths of MiMAG in comparison with related approaches on synthetic as well as real-world datasets.