AutoPart: Parameter-Free Graph Partitioning and Outlier Detection

@inproceedings{Chakrabarti2004AutoPartPG,
  title={AutoPart: Parameter-Free Graph Partitioning and Outlier Detection},
  author={Deepayan Chakrabarti},
  booktitle={PKDD},
  year={2004}
}
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
Highly Influential
This paper has highly influenced 11 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 178 citations. REVIEW CITATIONS
106 Citations
16 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 106 extracted citations

178 Citations

01020'06'09'12'15'18
Citations per Year
Semantic Scholar estimates that this publication has 178 citations based on the available data.

See our FAQ for additional information.

Similar Papers

Loading similar papers…