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Inferring Networks of Diffusion and Influence

2010

Conference Paper

ei


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.

Author(s): Gomez Rodriguez, M. and Leskovec, J. and Krause, A.
Journal: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2010)
Pages: 1019-1028
Year: 2010
Month: July
Day: 0
Editors: Rao, B. , B. Krishnapuram, A. Tomkins, Q. Yang
Publisher: ACM Press

Department(s): Empirical Inference
Research Project(s): Structure and Dynamics of Diffusion Networks
Bibtex Type: Conference Paper (inproceedings)

Address: New York, NY, USA
Digital: 0
DOI: 10.1145/1835804.1835933
Event Name: 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2010)
Event Place: Washington, DC, USA
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{6557,
  title = {Inferring Networks of Diffusion and Influence},
  author = {Gomez Rodriguez, M. and Leskovec, J. and Krause, A.},
  journal = {Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2010)},
  pages = {1019-1028},
  editors = {Rao, B. , B. Krishnapuram, A. Tomkins, Q. Yang},
  publisher = {ACM Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {New York, NY, USA},
  month = jul,
  year = {2010},
  month_numeric = {7}
}