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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/14127
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| Title: | Building an Effective Representation for Dynamic Networks |
| Authors: | Hill, Shawndra
Agarwal, Deepak
Bell, Robert
Volinsky, Chris |
| Keywords: | approximate subgraphs
dynamic graphs
exponential averaging
fraud detection
transactional data streams |
| Issue Date: | 19-Feb-2005 |
| Publisher: | Stern School of Business, New York University |
| Series/Report no.: | CeDER-05-11 |
| Abstract: | A dynamic network is a special type of network which is comprised of
connected transactors which have repeated evolving interaction. Data on
large dynamic networks such as telecommunications networks and the
Internet are pervasive. However, representing dynamic networks in a
manner that is conducive to efficient large-scale analysis is a
challenge. In this paper, we represent dynamic graphs using a data
structure introduced by Cortes et. a]. [Q]. We advocate their
representation because it accounts for the evolution of relationships
between transactors through time, mitigates noise at the local
transactor level, and allows for the removal of stale relationships. Our
work improves on their heuristic arguments by formalizing the
representation with three tunable parameters. In doing this, we develop
a generic framework for evaluating and tuning any dynamic graph. We show
that the storage saving approximations involved in the representation do
not affect predictive performance, and typically improve it. We motivate
our approach using a fraud detection example from the telecommunications
industry, and demonstrate that we can outperform published results on
the fraud detection task. In addition, we present preliminary analysis
on web logs and email networks. |
| URI: | http://hdl.handle.net/2451/14127 |
| Appears in Collections: | CeDER Working Papers
IOMS: Information Systems Working Papers
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