Title

Mining Indirect Antagonistic Communities from Social Interactions

Publication Type

Journal Article

Publication Date

2013

Abstract

Antagonistic communities refer to groups of people with opposite tastes, opinions, and factions within a community. Given a set of interactions among people in a community, we develop a novel pattern mining approach to mine a set of antagonistic communities. In particular, based on a set of user-specified thresholds, we extract a set of pairs of communities that behave in opposite ways with one another. We focus on extracting a compact lossless representation based on the concept of closed patterns to prevent exploding the number of mined antagonistic communities. We also present a variation of the algorithm using a divide and conquer strategy to handle large datasets when main memory is inadequate. The scalability of our approach is tested on synthetic datasets of various sizes mined using various parameters. Case studies on Amazon, Epinions, and Slashdot datasets further show the efficiency and the utility of our approach in extracting antagonistic communities from social interactions.

Keywords

antagonistic group, frequent pattern mining, closed pattern, social network mining

Discipline

Software Engineering

Research Areas

Software Systems

Publication

Knowledge and Information Systems

Volume

35

Issue

3

First Page

553

Last Page

583

ISSN

0219-1377

Identifier

10.1007/s10115-012-0519-4

Publisher

Springer Verlag

Additional URL

http://dx.doi.org/10.1007/s10115-012-0519-4