Summarizing and understanding large graphs-Reference-Cited by-同舟云学术

Summarizing and understanding large graphs

Published:2015-05-18 Issue:3 Volume:8 Page:183-202
ISSN:1932-1864
Container-title:Statistical Analysis and Data Mining: The ASA Data Science Journal
language:en
Short-container-title:Statistical Analy Data Mining

Author:

Koutra Danai¹,Kang U²,Vreeken Jilles³,Faloutsos Christos¹

Affiliation:

1. School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 USA

2. Department of Computer Science, KAIST Daejeon Republic of Korea

3. Databases and Information Systems Max Planck Institute for Informatics and Saarland University Saarbrücken Germany

Publisher

Wiley

Subject

Computer Science Applications,Information Systems,Analysis

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/sam.11267

Reference63 articles.

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2. D.Chakrabarti Y.Zhan D.Blandford C.Faloutsos andG.Blelloch Netmine: New mining tools for large graphs In SDM Workshop on Link Analysis Counter‐terrorism and Privacy 2004.

3. J.Leskovec K. J.Lang A.Dasgupta andM. W.Mahoney Statistical properties of community structure in large social and information networks In Proceedings of the 17th International Conference on World Wide Web (WWW) Beijing China 2008 695–704.

4. B. A.Prakash M.Seshadri A.Sridharan S.Machiraju andC.Faloutsos Eigenspokes: Surprising patterns and community structure in large graphs Proceedings of the 14th Pacific‐Asia Conference on Knowledge Discovery and Data Mining (PAKDD) Hyderabad India 2010.

5. G.KarypisandV.Kumar Multilevel‐way hypergraph partitioning In Proceedings of the 36th Conference on Design Automation(DAC) New Orleans LA 1999 343–348.

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