Detecting　pickpocketing　gangs　on　buses　is　critical　for　safety　and　public　security　department.　Knowing　this　both　in　real　time　and　from　historical　records　would　allow　effective　law　enforcement　and　crime　prevention.　However,　very　little　research　has　been　devoted　into　identifying　pickpocketing　gangs　in　an　automated　and　holistic　manner.　This　research　utilizes　smart　card　data　generated　by　bus　riders　to　identify　pickpocketing　gangs,　who　possess　distinct　characteristics　from　regular　passengers.　Particularly,　we　create　a　dataset　of　1,098　pickpockets　among　4.06　million　bus　riders　in　August,　2015　in　Beijing　automatically　and　efficiently　based　on　an　efficient　labeling　model　of　outliers.　This　model　examines　anomaly　of　passengers　using　the　so-called　relative　outlier　cluster　factor　and　local　outlier　factor.　The　proposed　mobility　patterns　of　pickpockets　are　then　learned　based　on　supervised　classification.　Pickpockets　from　the　derived　dataset　form　a　piekpocketing　network,　which　is　modeled　as　a　graph　with　vertices　denoted　as　discrete　pickpockets,　and　edge　weight　quantified　by　a　combined　similarity　on　mobility　pattern,　space　and　time.　A　graph-based　Louvain　algorithm　is　adopted　to　detect　pickpocketing　gangs.　Experiments　are　conducted　on　SINA　microblog　data　to　verify　the　detected　pickpocketing　gangs　identified　by　the　proposed　framework.　Results　show　that　the　framework　detects　63　pickpocketing　gangs　and　verifies　34　gangs　by　microblogs,　with　recall　value　0.85.　Findings　from　t　his　research　can　assist　police　and　public　safety　departments　in　the　city　in　taking　pro-active　actions　to　track　down　pickpocketing　gangs.