Trattner Christoph, Steurer Michael
2015
Detecting partnership in location-based and online social networks
Social Netw. Analys. Mining Springer
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.