计算机科学 ›› 2015, Vol. 42 ›› Issue (11): 149-153.doi: 10.11896/j.issn.1002-137X.2015.11.031
何旵阳,孙鲁敬,杨家海
HE Chan-yang, SUN Lu-jing and YANG Jia-hai
摘要： 从用户产生和消费Feeds的角度分析社交网络变得不活跃的原因,通过分析人人网某大学社区用户长周期的Feeds行为来探讨该社区用户活跃度的变化。通过对用户活跃性周期和Feeds时间间隔的分析,发现越来越多的用户产生Feeds的活跃度在下降,并导致其他用户接收到的信息流的流速和多样性下降。社交网络用户由于各种原因离开或变得不活跃,并通过信息流对其朋友圈形成负向反馈,这可能是社交网络变得不活跃的深层原因。模拟实验表明,30%的初始不活跃用户会使得整个社区的信息流快速下降,并导致整个社区不活跃。
| [1] Kumar S,Zafarani R,Liu H.Understanding User MigrationPatterns in Social Media[C]∥Proc.of AAAI .San Francisco,USA,Aug.2011:1204-1209 [2] Zhao X,Sala A,Wilson C,et al.Multi-scale dynamics in a massive online social network[C]∥Proc.of the 2012 ACM SIGCOMM conference on Internet measurement.Boston,USA,2012:171-184 [3] Gong N Z,Xu W,Huang L,et al.Evolution of social-attribute networks:measurements,modeling,and implications using google+[C]∥Proc.of the 2012 ACM conference on Internet measurement.Boston,USA,Nov.2012:131-144 [4] Leskovec J,Backstrom L,Kumar R,et al.Microscopic evolution of social networks[C]∥Proc.of the 14th ACM SIGKDD Int’l Conference on Knowledge Discovery and Data Mining.Las Vegas,USA,Aug.2008:462-470 [5] Wu S,Das Sarma A,Fabrikant A,et al.Arrival and departuredynamics in social networks[C]∥Proc.of the Sixth ACM Int’l Conference on Web Search and Data Mining.Rome,Italy,Feb.2013:233-243 [6] Dasgupta K,Singh R,Viswanathan B,et al.Social ties and their relevance to churn in mobile telecom networks[C]∥Proc.of the 11th int’l conference on Extending Database Technology:Advances in database technology.Nantes,France,Mar.2008:668-677 [7] Richter Y,Yom-Tov E,Slonim N.Predicting Customer Churn in Mobile Networks through Analysis of Social Groups[C]∥Proc.of SDM.Columbus,USA,Apr.2010:732-741 [8] Anagnostopoulos A,Kumar R,Mahdian M.Influence and correlation in social networks[C]∥Proc.of the 14th ACM SIGKDD Int’l Conference on Knowledge Discovery and Data Mining .Las Vegas,USA,Aug.2008:7-15 [9] Crandall D,Cosley D,Huttenlocher D,et al.Feedback effects between similarity and social influence in online communities[C]∥Proc.of the 14th ACM SIGKDD Int’l Conference on Knowledge Discovery and Data Mining.Las Vegas,USA,Aug.2008:160-168 [10] Backstrom L,Huttenlocher D,Kleinberg J,et al.Group formation in large social networks:membership,growth,and evolution[C]∥Proc.of the 12th ACM SIGKDD Int’l Conference on Knowledge discovery and data mining.Philadelphia,USA,Aug.2006:44-54 [11] Benevenuto F,Rodrigues T,Cha M,et al.Characterizing userbehavior in online social networks[C]∥Proc.of the 9th ACM SIGCOMM Conference on Internet Measurement.Chicago,USA.2009:49-62 [12] Schneider F,Feldmann A,Krishnamurthy B,et al.Understan-ding online social network usage from a network perspective[C]∥Proc.of the 9th ACM SIGCOMM Conference on Internet Measurement Conference.Chicago,USA,Nov.2009:36-48 [13] Mislove A,Marcon M,Gummadi K,et al.Measurement andanalysis of online social networks[C]∥Proc.of the 7th ACM SIGCOMM Conference on Internet Measurement.San Diego,USA,Oct.2007:29-42 [14] Freyne J,Berkovsky S,Daly E M,et al.Social networking feeds:recommending items of interest[C]∥Proc.of the Fourth ACM Conference on Recommender Systems.Barcelona,Spain,Sept.2010:277-280 [15] Soh P H,Lin Y C,Chen M S.Recommendation for online social feeds by exploiting user response behavior[C]∥Proc.of the 22nd International Conference on World Wide Web Companion.Rio De Janeiro,Brazil,May 2013:189-198 [16] Jiang J,Wilson C,Wang X,et al.Understanding latent interactions in online social networks[J].ACM Transactions on the Web (TWEB),2013,7(4):1-39 [17] Hu H B,Han D Y.Empirical analysis of individual popularity and activity on an online music service system[J].Physica A:Statistical Mechanics and its Applications,2008,387(23):5916-5921 [18] Clauset A,Shalizi C R,Newman M E.Power-law distributions in empirical data[J].SIAM review,2009,51(4):661-703 [19] Wikipedia.Diversity index.http://en.wikipedia.org/wiki/Diversity_index |
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