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
Martin Atzmueller , Research Center for Information System Design, University of Kassel, Germany
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.
Social network services, Markov processes, Data mining, Context, Adaptation models, Data models, Analytical models,
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