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Published Articles >> Table of Contents >> Abstract
2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05)
pp. 522-528
WICER: A Weighted Inter-Cluster Edge Ranking for Clustered Graphs
Divya Padmanabhan, University of Minnesota
Prasanna Desikan, University of Minnesota
Jaideep Srivastava, University of Minnesota
Kashif Riaz, University of Minnesota
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/WI.2005.166
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| Abstract |
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Several algorithms based on link analysis have been
developed to measure the importance of nodes on a graph
such as pages on the World Wide Web. PageRank and
HITS are the most popular ranking algorithms to rank the
nodes of any directed graph. But, both these algorithms
assign equal importance to all the edges and nodes,
ignoring the semantically rich information from nodes
and edges. Therefore, in the case of a graph containing
natural clusters, these algorithms do not differentiate
between inter-cluster edges and intra-cluster edges.
Based on this parameter, we propose a Weighted
Inter-Cluster Edge Ranking for clustered graphs that weighs
edges (based on whether it is an inter-cluster or an intra-cluster
edge) and nodes (based on the number of clusters
it connects). We introduce a parameter ‘ α’ which can be
adjusted depending on the bias desired in a clustered
graph. Our experiments were two fold. We implemented
our algorithm to relationship set representing legal
entities and documents and the results indicate the
significance of the weighted edge approach. We also
generated biased and random walks to quantitatively
study the performance.
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 Additional Information
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Citation:
Divya Padmanabhan, Prasanna Desikan, Jaideep Srivastava, Kashif Riaz,
"WICER: A Weighted Inter-Cluster Edge Ranking for Clustered Graphs,"
wi,
pp. 522-528,
2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05),
2005
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