Abstract:
:We are interested in tracking changes in large-scale data by periodically creating an agglomerative clustering and examining the evolution of clusters (communities) over time. We examine a large real-world data set: the NEC CiteSeer database, a linked network of >250,000 papers. Tracking changes over time requires a clustering algorithm that produces clusters stable under small perturbations of the input data. However, small perturbations of the CiteSeer data lead to significant changes to most of the clusters. One reason for this is that the order in which papers within communities are combined is somewhat arbitrary. However, certain subsets of papers, called natural communities, correspond to real structure in the CiteSeer database and thus appear in any clustering. By identifying the subset of clusters that remain stable under multiple clustering runs, we get the set of natural communities that we can track over time. We demonstrate that such natural communities allow us to identify emerging communities and track temporal changes in the underlying structure of our network data.
journal_name
Proc Natl Acad Sci U S Aauthors
Hopcroft J,Khan O,Kulis B,Selman Bdoi
10.1073/pnas.0307750100keywords:
subject
Has Abstractpub_date
2004-04-06 00:00:00pages
5249-53eissn
0027-8424issn
1091-6490pii
0307750100journal_volume
101 Suppl 1pub_type
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