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Building an Effective Representation for Dynamic Networks

33 Pages Posted: 9 Oct 2008  

Shawndra Hill

Microsoft Research

Deepak Agarwal

affiliation not provided to SSRN

Chris Volinsky

affiliation not provided to SSRN

Date Written: February 2005

Abstract

A dynamic network is a special type of network which is comprised of connected transactors whichhave repeated evolving interaction. Data on large dynamic networks such as telecommunications networksand the Internet are pervasive. However, representing dynamic networks in a manner that is conduciveto efficient large-scale analysis is a challenge. In this paper, we represent dynamic graphs using a datastructure introduced by Cortes et. a]. [Q]. We advocate their representation because it accounts forthe evolution of relationships between transactors through time, mitigates noise at the local transactorlevel, and allows for the removal of stale relationships. Our work improves on their heuristic argumentsby formalizing the representation with three tunable parameters. In doing this, we develop a genericframework for evaluating and tuning any dynamic graph. We show that the storage saving approximationsinvolved in the representation do not affect predictive performance, and typically improve it. We motivateour approach using a fraud detection example from the telecommunications industry, and demonstratethat we can outperform published results on the fraud detection task. In addition, we present preliminaryanalysis on web logs and email networks.

Keywords: approximate subgraphs, dynamic graphs, exponential averaging, fraud detection, transactional data streams

Suggested Citation

Hill, Shawndra and Agarwal, Deepak and Volinsky, Chris, Building an Effective Representation for Dynamic Networks (February 2005). Information Systems Working Papers Series, Vol. , pp. -, 2005. Available at SSRN: https://ssrn.com/abstract=1281322

Shawndra Hill (Contact Author)

Microsoft Research ( email )

New York, NY 10011
United States

Deepak Agarwal

affiliation not provided to SSRN ( email )

Chris Volinsky

affiliation not provided to SSRN

No Address Available

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