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Compositional Subgroup Discovery on Attributed Social Interaction Networks

  • Martin AtzmuellerEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11198)

Abstract

While standard methods for detecting subgroups on plain social networks focus on the network structure, attributed social networks allow compositional analysis, i. e., by exploiting attributive information. Accordingly, this paper applies a compositional perspective for identifying compositional subgroup patterns. In contrast to typical approaches for community detection and graph clustering it focuses on the dyadic structure of social interaction networks. For that, we adapt principles of subgroup discovery – a general data mining technique for the identification of local patterns – to the dyadic network setting. We focus on social interaction networks, where we specifically consider properties of those social interactions, i. e., duration and frequency. In particular, we present novel quality functions for estimating the interestingness of a subgroup and discuss their properties. Furthermore, we demonstrate the efficacy of the approach using two real-world datasets on face-to-face interactions.

Notes

Acknowledgements

This work has been partially supported by the German Research Foundation (DFG) project “MODUS” under grant AT 88/4-1.

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Authors and Affiliations

  1. 1.Department of Cognitive Science and Artificial IntelligenceTilburg UniversityTilburgThe Netherlands

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