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  1. Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis (WebKDD/SNA-KDD '07)
  2. Exploiting time-varying relationships in statistical relational models
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Learning to rank typed graph walks: local and global approaches
Exploiting time-varying relationships in statistical relational models
Community detection in large-scale social networks
Node roles and community structure in networks
Inferring agent dynamics from social communication network
Dynamics of collaborative document rating systems
Why we twitter: understanding microblogging usage and communities
Looking for great ideas: analyzing the innovation jam
DBconnect: mining research community on DBLP data
Spectral clustering in telephone call graphs
Applying link-based classification to label blogs
A recommender system based on local random walks and spectral methods
Automated social hierarchy detection through email network analysis

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Exploiting time-varying relationships in statistical relational models

Content Provider ACM Digital Library
Author Sharan, Umang Neville, Jennifer
Abstract In a growing number of relational domains, the data record temporal sequences of interactions among entities. For example, in citation domains authors publish scientific papers together each year and in telephone fraud detection domains people make calls to each other each day. The temporal dynamics of these interactions contain information that can improve predictive models (e.g., people publishing together frequently are likely to be publishing on the same topic) but to date there has been little effort to incorporate timevarying dependencies into relational models. Past work in relational learning has focused primarily on static "snapshots" of relational data. In this paper, we present an initial approach to modeling dynamic relational data graphs in predictive models of attributes. More specifically, we use a two-step process that first summarizes the dynamic graph with a weighted static graph and then incorporates the link weights in a relational Bayes classifier. We evaluate our approach on the Cora dataset (where co-author and citation links vary over time) showing that our approach results in significant performance gains over a baseline snapshot approach that ignores the temporal component of the data.
Starting Page 9
Ending Page 15
Page Count 7
File Format PDF
ISBN 9781595938480
DOI 10.1145/1348549.1348551
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2007-08-12
Publisher Place New York
Access Restriction Subscribed
Content Type Text
Resource Type Article
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