AutoPart: Parameter-Free Graph Partitioning and Outlier Detection

  title={AutoPart: Parameter-Free Graph Partitioning and Outlier Detection},
  author={Deepayan Chakrabarti},
Graphs arise in numerous applications, such as the analysis of the Web, router networks, social networks, co-citation graphs, etc. Virtually all the popular methods for analyzing such graphs, for example, k-means clustering, METIS graph partitioning and SVD/PCA, require the user to specify various parameters such as the number of clusters, number of partitions and number of principal components. We propose a novel way to group nodes, using information-theoretic principles to choose both the… CONTINUE READING
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