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Fast discovery of connection subgraphs
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Source International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining table of contents
Seattle, WA, USA
SESSION: Research track papers table of contents
Pages: 118 - 127  
Year of Publication: 2004
ISBN:1-58113-888-1
Authors
Christos Faloutsos  IBM Almaden Research Center
Kevin S. McCurley  IBM Almaden Research Center
Andrew Tomkins  IBM Almaden Research Center
Sponsors
SIGMOD: ACM Special Interest Group on Management of Data
SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 19,   Downloads (12 Months): 175,   Citation Count: 33
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ABSTRACT

We define a connection subgraph as a small subgraph of a large graph that best captures the relationship between two nodes. The primary motivation for this work is to provide a paradigm for exploration and knowledge discovery in large social networks graphs. We present a formal definition of this problem, and an ideal solution based on electricity analogues. We then show how to accelerate the computations, to produce approximate, but high-quality connection subgraphs in real time on very large (disk resident) graphs.We describe our operational prototype, and we demonstrate results on a social network graph derived from the World Wide Web. Our graph contains 15 million nodes and 96 million edges, and our system still produces quality responses within seconds.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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CITED BY  33

Collaborative Colleagues:
Christos Faloutsos: colleagues
Kevin S. McCurley: colleagues
Andrew Tomkins: colleagues