Social ties and checkin sites: Connections and latent structures in Location Based Social Networks Sudhir B Kylasa Giorgos Kollias Ananth Grama Elec. and Comp. Engg. Dept IBM T. J. Watson Research Center Computer Science Dept Purdue University Yorktown Heights Purdue University West Lafayette, Indiana 47907 NY 10598 West Lafayette, Indiana 47907 [email protected] [email protected] [email protected] Abstract—Location Based Social Networks (LBSNs) integrate the network. These structures can be leveraged for enhancing location-based facilities with social connectivity for delivering user experience, as well as optimizing flow of information and a variety of services, enhancing user experience, emergency/ influence. disaster management, and streamlining business processes. A number of recent research efforts have studied relationships In this paper, we present a statistical model and detailed between geolocation and social connectivity, social connectivity analysis using real social network data, of the interplay be- and preferences, and node attributes and strength of social ties. tween individual user attributes (checkin location information) These efforts have successfully demonstrated prediction of various and social ties. In particular, we examine the extent to which attributes based on social connectivity, mobility, dynamic checkin shared checkins are indicative of social connections and vice information etc., including prediction of user location as well as versa. We argue that typical LBSNs are composed of multi- future checkin locations. tiered networks that display markedly distinct structural and In this paper, we study the relationship between shared functional properties. We investigate the use of shared checkin checkin locations and the structure and nature of social ties. information for deconvolving the network, and analyze the We argue that typical LSBNs are in fact composed of layers of properties of these deconvolved networks in detail. Typically, networks of varying structure and function, and that it is possible a social network can be viewed as a super-position of different to deconcolve these networks through effective statistical analysis sub-networks with statistically different properties (clustering of shared checkins. In this context, we pose and validate the coefficient, etc). We show how one can identify constituent following hypotheses: (i) a large number of shared checkins imply social connectivity; however, social connectivity does not imply subnetworks, and how properties of these sub-networks can statistically large number of shared checkins; (ii) entities in social be leveraged for specific network functions. ties that share a large number of checkins tend to be strongly Using a Bayesian approach, we first assess checkin lo- clustered. We hypothesize that such strong ties (for example, cation sharing as a social connectivity predictor. Based on family ties, friendships etc.) carry higher influence compared to real datasets, we show that a social tie is probabilistically weaker ties (mere acquaintances) in the social network; and (iii) social ties that have statistically fewer shared checkins (weak implied by large shared checkin counts. However, the inverse ties) tend to be less clustered than the underlying (baseline) is not true – social connectivity does not probabilistically network. We hypothesize that such ties (for example professional imply geolocation similarity. We also demonstrate how social ties, friends of friends, acquaintances etc.,) carry less influence. connectivity can be augmented by shared checkin location data We present statistical models and validate our hypotheses on to deconvolve networks. We define a set of discrete intervals real datasets. Our conclusions can significantly enhance flow of on the number of shared checkins. We use these intervals to information and influence in the network by suitably leveraging deconvolve the base LBSN into separate layers. We argue the distinct relationships captured in the deconcolved networks. that these layers represent networks of different strength of Keywords—Location Based Social Networks, Friends network, social ties. Our argument is based on a sequence of interesting Social connectivity, Probabilistic estimation of social connectivity; observations regarding the network layers. We demonstrate that the network layers corresponding to the high shared checkin counts show high degree of triadic closure (clustering I. INTRODUCTION coefficient). This suggests strong social ties in these layers. In Location Based Social Networks (LBSNs) integrate contrast, network layers corresponding to low shared checkins geospatial data with social connectivity to enable users to tend to be sparser, with lower degrees of triadic closure than register location information, time tags, and share preferences. the base networks. This suggests that these network layers code The rich data model of LBSNs can be abstracted into a weak ties – acquaintances, friends of friends, etc. number of disparate views – a mapping of users to locations, Deconvolution of the LBSN in this manner has significant locations to events and associated times, and users to other utility. Network layers corresponding to strong ties carry users. Interdependencies between these views, either common- information and influence more efficiently than lower net- alities (base information) or divergence (true relation-specific work layers. This enables targeted information dissemination, content) reveal interesting and important latent structures in leading to higher network utilization. Our work provides a The authors would like to acknowledge the US National Science Foundation general framework within which other node attributes (other Grants CSR 1422338 and OIA 0939370. than checkin information) can be used to generalize from node to pairwise to (sub)community structure and function. Gu et al. [2] present analysis based on a combination of geo-sensitive textual features for improved text-based location The rest of the paper is organized as follows: Section II estimation. Cho et al. [3] explore human geographic movement initiates our discussion with an overview of related research. in relation to social ties to analyze future checkins of a user and Section III presents definitions and notation used in the rest of effects of distance between users on future checkins in a typical the paper. Section IV formalizes the main propositions put social network. Noulas et al. [4] analyze checkin dynamics forth in the paper. Section V presents detailed analyses of to study the spatio-temporal patterns of user mobility. In real datasets to validate our propositions. Section VI draws particular they use temporal checkin information to deduce conclusions and outlines avenues for future research. user mobility patterns for a recommender system to enrich user experience. Chang et al. [24] present a model for predicting II. RELATED WORK future checkins based on past checkins, time of checkin, and user demographics. Current research on LBSNs can be broadly classified into the following areas: (i) analyses of spatial properties of social Chang et al. [24] show that an increasing number of networks, (ii) inference and prediction of user attributes from shared checkins results in increasing friendship probabilities. social context (social ties, geotagged photos, etc.), (iii) infer- However, the relationship between social ties and number ence and prediction of attributes of social ties, such as mobility of shared checkins is not investigated. Pelechrinis and Kr- patterns, distance etc. and, (iv) inference and prediction of ishnamurthy [13], using affiliation networks, draw similar social ties and future checkins based on temporal aspects of conclusions as well. Their primary contribution is the interplay social networks (time of checkins, status updates, mobility between the nature of a checkin location and social ties. They patterns etc.). We briefly summarize results in each of these show that checkin locations have higher clustering coefficients categories and put our results in context. among friends, when compared to non-friends. Focal closure, which considers pairs of users and their social ties, as op- A large body of research [1]–[8] argues that distance posed to social closure is used extensively in drawing these is a controlling factor in a social network, and exhibits a conclusions. In our research social closures, in which social power-law relationship with different exponents. Scellato et ties between a set of users is considered, play a pivotal role al. [9] study social-spatial properties of networks and propose in defining characteristics of a social subgraph. a statistical model for the heterogeneity of social triads as a function of distance and probability of a link in a social triad. Our focus in this work differs from prior approaches in Other researchers [8], [10]–[12] conclude that social ties in that we analyze the impact of node (checkin) information on highly connected groups tend to span shorter distances, and the aggregate structure and function of the network. We show are more probable, compared to their long range counterparts. that there is a strong link between checkin information and Kaltenbrunner et al. [8] study the effect of geographic distance strength of ties, and that this link can be used to identify latent on online social interactions, and conclude that spatial prox- structures in networks. imity greatly impacts formation of social links; however, once formed other factors determine