基于会面合并事件的社会关系强度度量模型
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摘要
针对时空数据条件下的网络用户社会关系挖掘,该文提出了一种社会关系强度度量模型—EPTDD(熵-个人-时间-时长-直径)模型,在会面合并事件基础上,从位置、时间、用户等多方面综合考虑会面事件对社会关系强度的贡献。首先,对用户之间会面事件进行检测,并将发生时间相近的会面事件进行合并处理,得到更加接近现实情况的会面合并事件;之后,以位置熵、位置个人背景、时间、时长和直径5种要素对会面合并事件的权重进行刻画;最后综合上述要素,分别实现社会关系强度度量的无监督和有监督方法。在3个真实数据集上的实验结果表明,该文提出的EPTDD模型能够有效度量用户之间的社会关系强度,且优于现有方法。
In order to mining the social relationship between users based on spatio-temporal data, a novel entropy-personal-time-duration-diameter(EPTDD) model is proposed for measuring relationship strength in this paper. The model considers the effect on relationship measurement of meeting events from several different sides including location, time and user on the basis of merged meeting events. Firstly, meeting events are merged according to their occurring times to obtain merged meeting events that are more correlated with real life. Each merged meeting event is then weighted from location entropy factor, location personal factor, time factor, duration factor and diameter factor. Finally, the five factors are synthesized to obtain unsupervised and supervised methods for measuring social relationship. Experimental results on three different real datasets demonstrate that our methods perform significantly more favorable than existing methods on the effectiveness.
In order to mining the social relationship between users based on spatio-temporal data, a novel entropy-personal-time-duration-diameter(EPTDD) model is proposed for measuring relationship strength in this paper. The model considers the effect on relationship measurement of meeting events from several different sides including location, time and user on the basis of merged meeting events. Firstly, meeting events are merged according to their occurring times to obtain merged meeting events that are more correlated with real life. Each merged meeting event is then weighted from location entropy factor, location personal factor, time factor, duration factor and diameter factor. Finally, the five factors are synthesized to obtain unsupervised and supervised methods for measuring social relationship. Experimental results on three different real datasets demonstrate that our methods perform significantly more favorable than existing methods on the effectiveness.
引文
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[2]BAO J,ZHENG Y,WILKIE D,et al.Recommendations in location-based social networks:a survey[J].Geoinformatica,2015,19(3):525-565.
[3]WANG D,PEDRESCHI D,SONG C,et al.Human mobility,social ties,and link prediction[C]//ACM SIGKDDInternational Conference on Knowledge Discovery and Data Mining.San Diego,USA:ACM,2011:1100-1108.
[4]DHAR S,VARSHNEY U.Challenges and business models for mobile location-based services and advertising[J].Communications of the ACM,2011,54(5):121-128.
[5]ZHENG V W,ZHENG Y,XIE X,et al.Collaborative location and activity recommendations with gps history data[C]//International Conference on World Wide Web.Raleigh,North Carolina,USA:[s.n.],2010:1029-1038.
[6]HOLZBAUER B O,SZYMANSKI B K,NGUYEN T,et al.Social ties as predictors of economic development[M].[S.l.]:Springer International Publishing,2016:178-185.
[7]GE Y,XIONG H,LIU C,et al.A taxi driving fraud detection system[C]//International Conference on Data Mining.Vancouver:IEEE Computer Society,2011:181-190.
[8]DESCIOLI P,KURZBAN R,KOCH N,et al.Best friends alliances,friend ranking,and the myspace social network[J].Perspect Psychol SCI,2011,6(1):6-8.
[9]ZHANG D,VASILAKOS A V,XIONG H.Predicting location using mobile phone calls[J].ACM Sigcomm Computer Communication Review,2012,42(4):295-296.
[10]PANG J,ZHANG Y.Exploring communities for effective location prediction[C]//International World Wide Web Conference.Florence:ACM,2015:87-88.
[11]TANG J,CHANG Y,LIU H.Mining social media with social theories:a survey[J].ACM Sigkdd Explorations Newsletter,2014,15(2):20-29.
[12]CHO E,MYERS S A,LESKOVEC J.Friendship and mobility:User movement in location-based social networks[C]//Proceedings of the 17th ACM SIGKDDInternational Conference On Knowledge Discovery And Data Mining.[S.l.]:ACM,2011:1082-1090.
[13]EAGLE N,PENTLAND A,LAZER D.Inferring friendship network structure by using mobile phone data[J].Proceedings of the National Academy of Sciences of the United States of America,2009,106(36):15274-15278.
[14]CRANDALL D,BACKSTROM L,COSLEY D,et al.Inferring social ties from geographic coincidences[J].Proceedings of the National Academy of Sciences of the United States of America,2010,107(52):22436-22441.
[15]PHAM H,SHAHABI C,LIU Y.EBM:an entropy-based model to infer social strength from spatiotemporal data[C]//ACM SIGMOD International Conference on Management of Data.[S.l.]:ACM,2013:265-276.
[16]WANG H,LI Z,LEE W C.PGT:Measuring mobility relationship using personal,global and temporal factors[C]//International Conference on Data Mining.Atlantic:IEEE Computer Society,2014:570-579.
[17]ZHANG Y,PANG J.Distance and friendship:a distance-based model for link prediction in social networks[M].[S.l.]:Springer International Publishing,2015.
[18]VALVERDE-REBAZA J,ROCHE M,PONCELET P,et al.Exploiting social and mobility patterns for friendship prediction in location-based social networks[C]//International Conference on Pattern Recognition.Cancún,Mexico:IEEE,2016:2526-2531.
[19]CHENG R,PANG J,ZHANG Y.Inferring friendship from check-in data of location-based social networks[C]//IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.[S.l.]:IEEE,2015:1284-1291.
[20]HE H,GARCIA E A.Learning from imbalanced data[J].IEEE Transactions on Knowledge and Data Engineering,2009,21(9):1263-1284.