Social resilience in online communities: the autopsy of friendster

We empirically analyze five online communities: Friendster, Livejournal, Facebook, Orkut, and Myspace, to study how social networks decline. We define social resilience as the ability of a community to withstand changes. We do not argue about the cause of such changes, but concentrate on their impact. Changes may cause users to leave, which may trigger further leaves of others who lost connection to their friends. This may lead to cascades of users leaving. A social network is said to be resilient if the size of such cascades can be limited. To quantify resilience, we use the k-core analysis, to identify subsets of the network in which all users have at least k friends. These connections generate benefits (b) for each user, which have to outweigh the costs (c) of being a member of the network. If this difference is not positive, users leave. After all cascades, the remaining network is the k-core of the original network determined by the cost-to-benefit (c/b) ratio. By analysing the cumulative distribution of k-cores we are able to calculate the number of users remaining in each community. This allows us to infer the impact of the c/b ratio on the resilience of these online communities. We find that the different online communities have different k-core distributions. Consequently, similar changes in the c/b ratio have a different impact on the amount of active users. Further, our resilience analysis shows that the topology of a social network alone cannot explain its success of failure. As a case study, we focus on the evolution of Friendster. We identify time periods when new users entering the network observed an insufficient c/b ratio. This measure can be seen as a precursor of the later collapse of the community. Our analysis can be applied to estimate the impact of changes in the user interface, which may temporarily increase the c/b ratio, thus posing a threat for the community to shrink, or even to collapse.

[1]  Marcel Karnstedt,et al.  Churn in Social Networks: A Discussion Boards Case Study , 2010, 2010 IEEE Second International Conference on Social Computing.

[2]  Ben Y. Zhao,et al.  On the bursty evolution of online social networks , 2012, HotSocial '12.

[3]  Gunes Ercal,et al.  User Groups in Social Networks: An Experimental Study on YouTube , 2011, 2011 44th Hawaii International Conference on System Sciences.

[4]  Jon M. Kleinberg,et al.  Group formation in large social networks: membership, growth, and evolution , 2006, KDD '06.

[5]  Sougata Mukherjea,et al.  Social ties and their relevance to churn in mobile telecom networks , 2008, EDBT '08.

[6]  Meeyoung Cha,et al.  Modeling the Adoption of Innovations in the Presence of Geographic and Media Influences , 2011, PloS one.

[7]  Steven M. Bellovin,et al.  Facebook and privacy: it's complicated , 2012, SOUPS.

[8]  Jaideep Srivastava,et al.  Churn Prediction in MMORPGs: A Social Influence Based Approach , 2009, 2009 International Conference on Computational Science and Engineering.

[9]  Cristopher Moore,et al.  On the bias of traceroute sampling: or, power-law degree distributions in regular graphs , 2005, STOC '05.

[10]  Jon Kleinberg,et al.  Analysis of large-scale social and information networks , 2013, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[11]  Ben Y. Zhao,et al.  User interactions in social networks and their implications , 2009, EuroSys '09.

[12]  Krishna P. Gummadi,et al.  Analyzing facebook privacy settings: user expectations vs. reality , 2011, IMC '11.

[13]  Seungyeop Han,et al.  Analysis of topological characteristics of huge online social networking services , 2007, WWW '07.

[14]  Hosung Park,et al.  Sampling bias in user attribute estimation of OSNs , 2013, WWW '13 Companion.

[15]  Krishna P. Gummadi,et al.  The Emergence of Conventions in Online Social Networks , 2012, ICWSM.

[16]  Jure Leskovec,et al.  Planetary-scale views on a large instant-messaging network , 2008, WWW.

[17]  Filippo Menczer,et al.  Partisan asymmetries in online political activity , 2012, EPJ Data Science.

[18]  W. Adger Social and ecological resilience: are they related? , 2000 .

[19]  Tim Roughgarden,et al.  Preventing Unraveling in Social Networks: The Anchored k-Core Problem , 2012, SIAM J. Discret. Math..

[20]  Virgílio A. F. Almeida,et al.  Characterizing user behavior in online social networks , 2009, IMC '09.

[21]  B. Huberman Sociology of science: Big data deserve a bigger audience , 2012, Nature.

[22]  Frank Schweitzer,et al.  A k-shell decomposition method for weighted networks , 2012, ArXiv.

[23]  Silvio Lattanzi,et al.  Arrival and departure dynamics in social networks , 2013, WSDM '13.

[24]  Idan Szpektor,et al.  Churn prediction in new users of Yahoo! answers , 2012, WWW.

[25]  Frank Schweitzer,et al.  Political polarization and popularity in online participatory media: an integrated approach , 2012, PLEAD '12.

[26]  Tina Eliassi-Rad,et al.  Measuring tie strength in implicit social networks , 2011, WebSci '12.

[27]  Taieb Znati,et al.  Modeling Churn in P2P Networks , 2007, 40th Annual Simulation Symposium (ANSS'07).

[28]  F. Schweitzer,et al.  Emotional persistence in online chatting communities , 2012, Scientific Reports.

[29]  Krishna P. Gummadi,et al.  Simplifying friendlist management , 2012, WWW.

[30]  M. Porter,et al.  Critical Truths About Power Laws , 2012, Science.

[31]  Krishna P. Gummadi,et al.  Measurement and analysis of online social networks , 2007, IMC '07.

[32]  LeskovecJure,et al.  Defining and evaluating network communities based on ground-truth , 2015 .

[33]  Alessandro Vespignani,et al.  Epidemic dynamics in finite size scale-free networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[34]  Lucas C. Parra,et al.  Origins of power-law degree distribution in the heterogeneity of human activity in social networks , 2013, Scientific Reports.

[35]  Lev Muchnik,et al.  Identifying influential spreaders in complex networks , 2010, 1001.5285.

[36]  Lise Getoor,et al.  Co-evolution of social and affiliation networks , 2009, KDD.

[37]  Mark E. J. Newman,et al.  Power-Law Distributions in Empirical Data , 2007, SIAM Rev..

[38]  Rakesh Agrawal,et al.  On participation in group chats on Twitter , 2013, WWW.

[39]  Minas Gjoka,et al.  Walking in Facebook: A Case Study of Unbiased Sampling of OSNs , 2010, 2010 Proceedings IEEE INFOCOM.

[40]  Andrew Harkins,et al.  Network Games with Perfect Complements , 2013 .

[41]  Stephen B. Seidman,et al.  Network structure and minimum degree , 1983 .

[42]  Krishna P. Gummadi,et al.  A measurement-driven analysis of information propagation in the flickr social network , 2009, WWW '09.

[43]  virginielariviere Social and ecological resilience: are they related? , 2010 .

[44]  Krishna P. Gummadi,et al.  On the evolution of user interaction in Facebook , 2009, WOSN '09.

[45]  Jure Leskovec,et al.  The life and death of online groups: predicting group growth and longevity , 2012, WSDM '12.

[46]  Hawoong Jeong,et al.  Comparison of online social relations in volume vs interaction: a case study of cyworld , 2008, IMC '08.