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2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2016)
San Francisco, CA, USA
Aug. 18, 2016 to Aug. 21, 2016
ISBN: 978-1-5090-2847-4
pp: 757-764
Martin Atzmueller , Research Center for Information System Design, University of Kassel, Germany
ABSTRACT
We present a new method for detecting descriptive community patterns capturing exceptional (sequential) link trails. For that, we provide a novel problem formalization: We model sequential data as first-order Markov chain models, mapped to an attributed weighted network represented as a graph. Then, we detect subgraphs (communities) using exceptional model mining techniques: We target subsets of sequential transitions between nodes that are exceptional in that sense that they either conform strongly to a specific reference or show significant deviations, estimated by a quality measure. In particular, such a community is described by a community pattern composed of descriptive features (of the attributed graph) covering the respective community. We present a comprehensive modeling approach and discuss results of a case study analyzing data from two real-world social networks.
INDEX TERMS
Social network services, Markov processes, Data mining, Context, Adaptation models, Data models, Analytical models,
CITATION
Martin Atzmueller, "Detecting community patterns capturing exceptional link trails", 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), vol. 00, no. , pp. 757-764, 2016, doi:10.1109/ASONAM.2016.7752323
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