Community Detection and Analysis on Attributed Social Networks

Atzmueller, Martin

doi:10.1007/978-1-4614-7163-9_110194-1

Cited by 2 publications

(2 citation statements)

References 58 publications

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“…Some approaches combine Subgroup Discovery and Network Science. In 2013, Atzmueller [2] gave an overview of data mining in social interaction networks, specifically human behavioural (offline) networks. Methods and approaches for describing and characterizing networks and their properties were proposed.…”

Section: Network Sciencementioning

confidence: 99%

Mining Exceptional Social Behaviour

Jorge

Atzmueller

Heravi

et al. 2019

Progress in Artificial Intelligence

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Essentially, our lives are made of social interactions. These can be recorded through personal gadgets as well as sensors adequately attached to people for research purposes. In particular, such sensors may record real time location of people. This location data can then be used to infer interactions, which may be translated into behavioural patterns. In this paper, we focus on the automatic discovery of exceptional social behaviour from spatio-temporal data. For that, we propose a method for Exceptional Behaviour Discovery (EBD). The proposed method combines Subgroup Discovery and Network Science techniques for finding social behaviour that deviates from the norm. In particular, it transforms movement and demographic data into attributed social interaction networks, and returns descriptive subgroups. We applied the proposed method on two real datasets containing location data from children playing in the school playground. Our results indicate that this is a valid approach which is able to obtain meaningful knowledge from the data.

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Section: Network Sciencementioning

confidence: 99%

Mining Exceptional Social Behaviour

Jorge

Atzmueller

Heravi

et al. 2019

Progress in Artificial Intelligence

Self Cite

View full text Add to dashboard Cite

show abstract

“…The analysis of complex networks, e.g., by investigating structural properties and identifying interesting patterns, is an important task to make sense of such networks, in order to ultimately enable an understanding of their phenomena and structures, e.g., (Newman 2003;Kumar et al 2006;Almendral et al 2007;Mitzlaff et al 2011;Mitzlaff et al 2013;Atzmueller 2014;Pool et al 2014;Galbrun et al 2014;Mitzlaff et al 2014;Kibanov et al 2014;Soldano et al 2015;Atzmueller et al 2016;Bendimerad et al 2016;Kaytoue et al 2017;Atzmueller 2017;2019). In this context, data mining on such networks represented as attributed graphs has recently emerged as a prominent research topic, e.g., (Moser et al 2009;Atzmueller 2014;Galbrun et al 2014;Soldano et al 2015;Atzmueller et al 2016;Bendimerad et al 2016;Kaytoue et al 2017). Methods for mining attributed graphs focus on the identification and extraction of patterns using topological information as well as compositional information on nodes and/or edges given by a set of attributes, e.g., (Atzmueller 2018;Wasserman and Faust 1994).…”

Section: Introductionmentioning

confidence: 99%

MinerLSD: efficient mining of local patterns on attributed networks

et al. 2019

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Local pattern mining on attributed networks is an important and interesting research area combining ideas from network analysis and data mining. In particular, local patterns on attributed networks allow both the characterization in terms of their structural (topological) as well as compositional features. In this paper, we present MinerLSD, a method for efficient local pattern mining on attributed networks. In order to prevent the typical pattern explosion in pattern mining, we employ closed patterns for focusing pattern exploration. In addition, we exploit efficient techniques for pruning the pattern space: We adapt a local variant of the standard Modularity metric used in community detection that is extended using optimistic estimates, and furthermore include graph abstractions. Our experiments on several standard datasets demonstrate the efficacy of our proposed novel method MinerLSD as an efficient method for local pattern mining on attributed networks.

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Community Detection and Analysis on Attributed Social Networks

Cited by 2 publications

References 58 publications

Mining Exceptional Social Behaviour

Mining Exceptional Social Behaviour

MinerLSD: efficient mining of local patterns on attributed networks

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