Influence and correlation in social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Feedback effects between similarity and social influence in online communities
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Social influence analysis in large-scale networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Social influence and the diffusion of user-created content
Proceedings of the 10th ACM conference on Electronic commerce
Bridging the gap between physical location and online social networks
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Everyone's an influencer: quantifying influence on twitter
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Exploiting place features in link prediction on location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Friendship and mobility: user movement in location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Human mobility, social ties, and link prediction
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
The structure of online diffusion networks
Proceedings of the 13th ACM Conference on Electronic Commerce
Information diffusion and external influence in networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Location affiliation networks: bonding social and spatial information
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Confluence: conformity influence in large social networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Homophily is a phenomenon observed very frequently in social networks and is related with the inclination of people to be involved with others that exhibit similar characteristics. The roots of homophily can be subtle and are mainly traced back to two mechanisms: (i) social selection and (ii) peer influence. Decomposing the effects of each of these mechanisms requires analysis of longitudinal data. This has been a burden to similar studies in traditional social sciences due to the hardness of collecting such information. However, the proliferation of online social media has enabled the collection of massive amounts of information related with human activities. In this work, we are interested in examining the forces of the above mechanisms in the context of the locations visited by people. For our study, we use a longitudinal dataset collected from Gowalla, a location-based social network (LBSN). LBSNs, unlike other online social media, bond users' online interactions with their activities in real-world, physical locations. Prior work on LBSNs has focused on the influence of geographical constraints on the formation of social ties. On the contrary, in this paper, we perform a microscopic study of the peer influence and social selection mechanisms in LBSNs. Our analysis indicates that while the similarity of friends' spatial trails at a geographically global scale cannot be attributed to peer influence, the latter can explain up to 40% of the geographically localized similarity between friends. Moreover, this percentage depends on the type of locations we examine, and it can be even higher for specific categories (e.g., nightlife spots). Finally, we find that the social selection mechanism, is only triggered by places that exhibit specific network characteristics. We believe that our work can have significant implications on obtaining a deeper understanding of the way that people create friendships, act and move in real space, which can further facilitate and enhance applications such as recommender systems, trip planning and marketing.