In recent years there has been a rapid growth of location-based social networking services
such as Foursquare and Facebook Places which have attracted an increasing number of users and
greatly enriched their urban experience. Typical location-based social networking sites allow a
user to check in at a real-world POI (point of interest e.g. a hotel restaurant theater
etc.) leave tips toward the POI and share the check-in with their online friends. The
check-in action bridges the gap between real world and online social networks resulting in a
new type of social networks namely location-based social networks (LBSNs). Compared to
traditional GPS data location-based social networks data contains unique properties with
abundant heterogeneous information to reveal human mobility i.e. when and where a user (who)
has been to for what corresponding to an unprecedented opportunity to better understand human
mobility from spatial temporal social and content aspects. The mining and understanding of
human mobility can further lead to effective approaches to improve current location-based
services from mobile marketing to recommender systems providing users more convenient life
experience than before. This book takes a data mining perspective to offer an overview of
studying human mobility in location-based social networks and illuminate a wide range of
related computational tasks. It introduces basic concepts elaborates associated challenges
reviews state-of-the-art algorithms with illustrative examples and real-world LBSN datasets
and discusses effective evaluation methods in mining human mobility. In particular we
illustrate unique characteristics and research opportunities of LBSN data present
representative tasks of mining human mobility on location-based social networks including
capturing user mobility patterns to understand when and where a user commonly goes (location
prediction) and exploiting user preferences and location profiles to investigate where and
when a user wants to explore (location recommendation) along with studying a user's check-in
activity in terms of why a user goes to a certain location.
