NDLI: Measuring Similarity between Sets of Overlapping Clusters

Content Provider	IEEE Xplore Digital Library
Author	Goldberg, M.K. Hayvanovych, M. Magdon-Ismail, M.
Copyright Year	2010
Abstract	The typical task of unsupervised learning is to organize data, for example into clusters, typically disjoint clusters (eg. the \math{K}-means algorithm). One would expect (for example) a clustering of books into topics to present overlapping clusters. The situation is even more so in social networks, a source of ever increasing data. Finding the groups or communities in social networks based on interactions between individuals (a measure of similarity) is an unsupervised learning task; and, groups overlap -- an individual can be a chess player and a violin player, in which case he would interact with members of both these groups. The problem we address is not that of finding the overlapping clusters, but of \emph{comparing} two sets of overlapping clusters. Such a task is the basis for comparing two different clusterings, which is important for comparing algorithms with each other or with a ground truth. From the social network point of view, we are particularly interested in quantifying social group evolution -- how much the social group structure of a social network changed -- by comparing the set of groups at consecutive time intervals. There is significant prior work on comparing sets of disjoint clusters (partitions). When overlap is allowed, the problem becomes considerably more complex owing to the possibility of degeneracies, which we illustrate through examples. We describe three novel definitions of the distance between collections of potentially overlapping sets, and present algorithms for computing those distances. We test our algorithms on diverse data sets: collections composed from social groups in Twitter, Blogosphere and Enron Email data.
Starting Page	303
Ending Page	308
File Size	285628
Page Count	6
File Format	PDF
ISBN	9781424484393
DOI	10.1109/SocialCom.2010.50
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Social network services Communities Clustering algorithms Information services Entropy Internet Web sites
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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