Between Min Cut and Graph Bisection
MFCS '93 Proceedings of the 18th International Symposium on Mathematical Foundations of Computer Science
On clusterings: Good, bad and spectral
Journal of the ACM (JACM)
Characterizing and Mining the Citation Graph of the Computer Science Literature
Knowledge and Information Systems
Network Analysis: Methodological Foundations (Lecture Notes in Computer Science)
Network Analysis: Methodological Foundations (Lecture Notes in Computer Science)
Engineering graph clustering: Models and experimental evaluation
Journal of Experimental Algorithmics (JEA)
IEEE Transactions on Knowledge and Data Engineering
Significance-Driven Graph Clustering
AAIM '07 Proceedings of the 3rd international conference on Algorithmic Aspects in Information and Management
Modularity-driven clustering of dynamic graphs
SEA'10 Proceedings of the 9th international conference on Experimental Algorithms
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During the last years, a wide range of huge networks has been made available to researchers. The discovery of natural groups, a task called graph clustering , in such datasets is a challenge arising in many applications such as the analysis of neural, social, and communication networks. We here present Orca , a new graph clustering algorithm, which operates locally and hierarchically contracts the input. In contrast to most existing graph clustering algorithms, which operate globally, Orca is able to cluster inputs with hundreds of millions of edges in less than 2.5 hours, identifying clusterings with measurably high quality. Our approach explicitly avoids maximizing any single index value such as modularity , but instead relies on simple and sound structural operations. We present and discuss the Orca algorithm and evaluate its performance with respect to both clustering quality and running time, compared to other graph clustering algorithms.