NDLI: Arc-Community Detection via Triangular Random Walks

Content Provider	IEEE Xplore Digital Library
Author	Boldi, P. Rosa, M.
Copyright Year	2012
Abstract	Community detection in social networks is a topic of central importance in modern graph mining, and the existence of overlapping communities has recently given rise to new interest in arc clustering. In this paper, we propose the notion of triangular random walk as a way to unveil arc-community structure in social graphs: a triangular walk is a random process that insists differently on arcs that close a triangle. We prove that triangular walks can be used effectively, by translating them into a standard weighted random walk on the line graph, our experiments show that the weights so defined are in fact very helpful in determining the similarity between arcs and yield high-quality clustering. Even if our technique gives a weighting scheme on the line graph and can be combined with any node-clustering method in the final phase, to make our approach more scalable we also propose an algorithm (ALP) that produces the clustering directly without the need to build the weighted line graph explicitly. Our experiments show that ALP, besides providing the largest accuracy, it is also the fastest and most scalable among all arc-clustering algorithms we are aware of.
Starting Page	48
Ending Page	56
File Size	363360
Page Count	9
File Format	PDF
ISBN	9781467344739
DOI	10.1109/LA-WEB.2012.19
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-25
Publisher Place	Colombia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Damping Algorithms Social network services Communities Clustering algorithms Stochastic processes Complex networks Social networks Experimentation Standards
Content Type	Text
Resource Type	Article

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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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