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  1. Proceedings of the 3rd international workshop on Link discovery (LinkKDD '05)
  2. Tuning representations of dynamic network data
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Bayes net graphs to understand co-authorship networks?
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Email alias detection using social network analysis
Mining hidden community in heterogeneous social networks
GiveALink: mining a semantic network of bookmarks for web search and recommendation
Discovering important nodes through graph entropy the case of Enron email database
A latent mixed membership model for relational data
Discovering missing links in Wikipedia

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Tuning representations of dynamic network data

Content Provider ACM Digital Library
Author Volinsky, Chris Bell, Robert Agarwal, Deepak Hill, Shawndra
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 effcient large-scale analysis is a challenge. In this paper, we represent dynamic graphs using a data structure introduced by Cortes et. al. [3]. 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.
Starting Page 25
Ending Page 27
Page Count 3
File Format PDF
ISBN 1595932151
DOI 10.1145/1134271.1134275
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2005-08-21
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Link prediction Approximate subgraphs Transactional data streams Statistical relational learning Link analysis Exponential averaging Dynamic graphs Fraud detection Graph matching
Content Type Text
Resource Type Article
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