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Detecting partnership in location-based and online social networks

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Existing approaches to identify the tie strength between users involve typically only one type of network. To date, no studies exist that investigate the intensity of social relations and in particular partnership between users across social networks. To fill this gap in the literature, we studied over 50 social proximity features to detect the tie strength of users defined as partnership in two different types of networks: location-based and online social networks. We compared user pairs in terms of partners and non-partners and found significant differences between those users. Following these observations, we evaluated the social proximity of users via supervised and unsupervised learning approaches and establish that location-based social networks have a great potential for the identification of a partner relationship. In particular, we established that location-based social networks and correspondingly induced features based on events attended by users could identify partnership with 0.922 AUC, while online social network data had a classification power of 0.892 AUC. When utilizing data from both types of networks, a partnership could be identified to a great extent with 0.946 AUC. This article is relevant for engineers, researchers and teachers who are interested in social network analysis and mining.

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This work is supported by the Know-Center and the EU funded projects Learning Layers (Grant Agreement 318209). Moreover, parts of this work were carried out during the tenure of an ERCIM “Alain Bensoussan” fellowship program by the first author. The Know-Center is funded within the Austrian COMET Program—Competence Centers for Excellent Technologies—under the auspices of the Austrian Ministry of Transport, Innovation and Technology, the Austrian Ministry of Economics and Labor and by the State of Styria. COMET is managed by the Austrian Research Promotion Agency (FFG).

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Correspondence to Christoph Trattner.

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This article is based on the paper “Acquaintance or Partner? Predicting Partnership in Online and Location-based Social Networks” published in the Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.

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Trattner, C., Steurer, M. Detecting partnership in location-based and online social networks. Soc. Netw. Anal. Min. 5, 42 (2015).

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