دانلود رایگان مقاله شبکه های وابستگی اجتماعی فضایی

Abstract

Location-based social networks (LBSNs) have recently attracted a lot of attention due to the number of novel services they can offer. Prior work on analysis of LBSNs has mainly focused on the social part of these systems. Even though it is important to know how different the structure of the social graph of an LBSN is as compared to the friendship-based social networks (SNs), it raises the interesting question of what kinds of linkages exist between locations and friendships. The main problem we are investigating is to identify such connections between the social and the spatial planes of an LBSN. In particular, in this paper we focus on answering the following general question “What are the bonds between the social and spatial information in an LBSN and what are the metrics that can reveal them?” In order to tackle this problem, we employ the idea of affiliation networks. Analyzing a dataset from a specific LBSN (Gowalla), we make two main interesting observations; (i) the social network exhibits signs of homophily with regards to the “places/venues” visited by the users, and (ii) the “nature” of the visited venues that are common to users is powerful and informative in revealing the social/spatial linkages. We further show that the “entropy” of a venue can be used to better connect spatial information with the existing social relations. The entropy records the diversity of a venue and requires only location history of users (it does not need temporal history). Finally, we provide a simple application of our findings for predicting existing friendship relations based on users’ historic spatial information. We show that even with simple unsupervised or supervised learning models we can achieve significant improvement in prediction when we consider features that capture the “nature” of the venue as compared to the case where only apparent properties of the location history are used (e.g., number of common visits).

8. Conclusions

In this paper we model an LBSN as a socio-spatial affiliation network and by analyzing data from a commercial network we identify bonds between the social and spatial information plane of the system. We find that friends exhibit in general much larger similarity with regards to the percentage of common venues visited, as compared to non-friends. However, considering only the number of common venues between two users, is not very helpful for strongly tying the two components of the network. Even though user similarity can provide a better bonding, the diversity of these common venues with regards to people visiting them is more informative and connect these two components better. This is also supported by the evaluations and results from simple, social link classifiers.