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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. Spectral clustering in telephone call graphs
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Learning to rank typed graph walks: local and global approaches
Exploiting time-varying relationships in statistical relational models
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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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Spectral clustering in telephone call graphs

Content Provider ACM Digital Library
Author Csalogany, Karoly Kurucz, Miklos Benczur, Andras Lukacs, Laszlo
Abstract We evaluate various heuristics for hierarchical spectral clustering in large telephone call graphs. Spectral clustering without additional heuristics often produces very uneven cluster sizes or low quality clusters that may consist of several disconnected components, a fact that appears to be common for several data sources but, to our knowledge, not described in the literature. Divide-and-Merge, a recently described postfiltering procedure may be used to eliminate bad quality branches in a binary tree hierarchy. We propose an alternate solution that enables k-way cuts in each step by immediately filtering unbalanced or low quality clusters before splitting them further. Our experiments are performed on graphs with various weight and normalization built based on call detail records. We investigate a period of eight months of more than two millions of Hungarian landline telephone users. We measure clustering quality both by cluster ratio as well as by the geographic homogeneity of the clusters obtained from telephone location data. Although divide-and-merge optimizes its clusters for cluster ratio, our method produces clusters of similar ratio much faster, furthermore we give geographically much more homogeneous clusters with the size distribution of our clusters resembling to that of the settlement structure.
Starting Page 82
Ending Page 91
Page Count 10
File Format PDF
ISBN 9781595938480
DOI 10.1145/1348549.1348559
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2007-08-12
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Telephone call graph Sociodemographic exploration Social networks Social network mining Spectral clustering Clustering
Content Type Text
Resource Type Article
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