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  Inferring Networks of Diffusion and Influence

Gomez Rodriguez, M., Leskovec, J., & Krause, A. (2010). Inferring Networks of Diffusion and Influence. In B. Rao, B. Krishnapuram, A. Tomkins, & Q. Yang (Eds.), KDD '10: 16th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1019-1028). New York, NY, USA: ACM Press.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BF3E-2 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-81A2-4
Genre: Conference Paper

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 Creators:
Gomez Rodriguez, M1, 2, Author              
Leskovec, J, Author
Krause, A, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Information diffusion and virus propagation are fundamental processes talking place in networks. While it is often possible to directly observe when nodes become infected, observing individual transmissions (i.e., who infects whom or who influences whom) is typically very difficult. Furthermore, in many applications, the underlying network over which the diffusions and propagations spread is actually unobserved. We tackle these challenges by developing a method for tracing paths of diffusion and influence through networks and inferring the networks over which contagions propagate. Given the times when nodes adopt pieces of information or become infected, we identify the optimal network that best explains the observed infection times. Since the optimization problem is NP-hard to solve exactly, we develop an efficient approximation algorithm that scales to large datasets and in practice gives provably near-optimal performance. We demonstrate the effectiveness of our approach by tracing information cascades in a set of 170 million blogs and news articles over a one year period to infer how information flows through the online media space. We find that the diffusion network of news tends to have a core-periphery structure with a small set of core media sites that diffuse information to the rest of the Web. These sites tend to have stable circles of influence with more general news media sites acting as connectors between them.

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 Dates: 2010-07
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1145/1835804.1835933
BibTex Citekey: 6557
 Degree: -

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Title: 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2010)
Place of Event: Washington, DC, USA
Start-/End Date: 2010-07-25 - 2010-07-28

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Title: KDD '10: 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Source Genre: Proceedings
 Creator(s):
Rao, B, Editor
Krishnapuram , B, Editor
Tomkins, A, Editor
Yang, Q, Editor
Affiliations:
-
Publ. Info: New York, NY, USA : ACM Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1019 - 1028 Identifier: ISBN: 978-1-4503-0055-1