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Geographical influence has been intensively exploited for location recommendations in location-based social networks (LBSNs) due to the fact that geographical proximity significantly affects users’ check-in behaviors. However, current studies only model the geographical influence on all users’ check-in behaviors as a universal way. We argue that the geographical influence on users’ check-in behaviors...
Location based social networks (LBSNs) are becoming increasingly popular with the fast deployment of broadband mobile networks and the growing prevalence of versatile mobile devices. This success has attracted great interest in studying and measuring the characteristics of LBSNs, such as Facebook Places, Yelp, and Google+ Local. However, it is often prohibitive, and sometimes too costly, to obtain...
Sampling of large social graphs is used for addressing infeasibility of measurements in large social graphs, or for crawling graphs from online social network services where accessing an entire social graph at once is often impossible. Sampling algorithms aim at maintaining certain properties of the original graphs in the sampled (or crawled) ones. Several sampling algorithms, such as breadth-first...
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