Temporal patterns behind the strength of persistent ties

Social networks are made out of strong and weak ties having very different structural and dynamical properties. But what features of human interaction build a strong tie? Here we approach this question from a practical way by finding what are the properties of social interactions that make ties more persistent and thus stronger to maintain social interactions in the future. Using a large longitudinal mobile phone database we build a predictive model of tie persistence based on intensity, intimacy, structural and temporal patterns of social interaction. While our results confirm that structural (embeddedness) and intensity (number of calls) features are correlated with tie persistence, temporal features of communication events are better and more efficient predictors for tie persistence. Specifically, although communication within ties is always bursty we find that ties that are more bursty than the average are more likely to decay, signaling that tie strength is not only reflected in the intensity or topology of the network, but also on how individuals distribute time or attention across their relationships. We also found that stable relationships have and require a constant rhythm and if communication is halted for more than 8 times the previous communication frequency, most likely the tie will decay. Our results not only are important to understand the strength of social relationships but also to unveil the entanglement between the different temporal scales in networks, from microscopic tie burstiness and rhythm to macroscopic network evolution.

[1]  Jari Saramäki,et al.  Small But Slow World: How Network Topology and Burstiness Slow Down Spreading , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[2]  Jari Saramäki,et al.  From seconds to months: an overview of multi-scale dynamics of mobile telephone calls , 2015, The European Physical Journal B.

[3]  Robin I. M. Dunbar,et al.  Communication in social networks: Effects of kinship, network size, and emotional closeness , 2011 .

[4]  Albert-László Barabási,et al.  Sex differences in intimate relationships , 2012, Scientific Reports.

[5]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[6]  Ronald S. Burt,et al.  Bridge decay , 2002, Soc. Networks.

[7]  Gueorgi Kossinets,et al.  Empirical Analysis of an Evolving Social Network , 2006, Science.

[8]  C. Rodriguez-Sickert,et al.  The dynamics of a mobile phone network , 2007, 0712.4031.

[9]  Eric Gilbert,et al.  Predicting tie strength with social media , 2009, CHI.

[10]  Filippo Menczer,et al.  Attention on Weak Ties in Social and Communication Networks , 2015, ArXiv.

[11]  Petter Holme,et al.  Temporal network structures controlling disease spreading. , 2016, Physical review. E.

[12]  G. Miritello Temporal Patterns of Communication in Social Networks , 2013 .

[13]  M. Hallinan The process of friendship formation , 1978 .

[14]  Renaud Lambiotte,et al.  Predicting links in ego-networks using temporal information , 2015, EPJ Data Science.

[15]  David Liben-Nowell,et al.  The link-prediction problem for social networks , 2007 .

[16]  David G. Rand,et al.  Dynamic social networks promote cooperation in experiments with humans , 2011, Proceedings of the National Academy of Sciences.

[17]  Manuel Cebrián,et al.  Social Media Fingerprints of Unemployment , 2014, PloS one.

[18]  Jari Saramäki,et al.  Daily Rhythms in Mobile Telephone Communication , 2015, PloS one.

[19]  Jari Saramäki,et al.  Digital daily cycles of individuals , 2015, Front. Phys..

[20]  M. Sambataro,et al.  Iterative variational approach to finite many-body systems , 2011 .

[21]  Alex Pentland,et al.  Using sociometers to quantify social interaction patterns , 2014, Scientific Reports.

[22]  Jari Saramäki,et al.  Temporal Networks , 2011, Encyclopedia of Social Network Analysis and Mining.

[23]  Hui Chen,et al.  A literature survey on smart cities , 2015, Science China Information Sciences.

[24]  Ronald S. Burt,et al.  Decay functions , 2000, Soc. Networks.

[25]  Alex Pentland,et al.  Sensing the "Health State" of a Community , 2012, IEEE Pervasive Computing.

[26]  Manuel Cebrián,et al.  Limited communication capacity unveils strategies for human interaction , 2013, Scientific Reports.

[27]  Daniele Quercia,et al.  Loosing "friends" on Facebook , 2012, WebSci '12.

[28]  Nitesh V. Chawla,et al.  Inferring Unusual Crowd Events from Mobile Phone Call Detail Records , 2015, ECML/PKDD.

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

[30]  Jon Kleinberg,et al.  The link prediction problem for social networks , 2003, CIKM '03.

[31]  Jari Saramäki,et al.  Persistence of social signatures in human communication , 2012, Proceedings of the National Academy of Sciences.

[32]  A-L Barabási,et al.  Structure and tie strengths in mobile communication networks , 2006, Proceedings of the National Academy of Sciences.

[33]  Sara B. Soderstrom,et al.  Dynamics of Dyads in Social Networks: Assortative, Relational, and Proximity Mechanisms , 2010 .

[34]  Nitesh V. Chawla,et al.  Predictors of short-term decay of cell phone contacts in a large scale communication network , 2011, Soc. Networks.

[35]  Nicola Perra,et al.  Burstiness and tie activation strategies in time-varying social networks , 2016, Scientific Reports.

[36]  Jon M. Kleinberg,et al.  The link-prediction problem for social networks , 2007, J. Assoc. Inf. Sci. Technol..

[37]  Kwang-Il Goh,et al.  Burstiness and memory in complex systems , 2006 .

[38]  Esteban Moro Egido,et al.  The dynamical strength of social ties in information spreading , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[39]  Albert-László Barabási,et al.  The origin of bursts and heavy tails in human dynamics , 2005, Nature.

[40]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[41]  Mark S. Granovetter The Strength of Weak Ties , 1973, American Journal of Sociology.

[42]  J. Kofler,et al.  Completely device-independent quantum key distribution , 2015, Physical Review A.

[43]  Albert-László Barabási,et al.  Universal features of correlated bursty behaviour , 2011, Scientific Reports.

[44]  Charo I. del Genio,et al.  Analysis of the communities of an urban mobile phone network , 2017, PloS one.

[45]  Haewoon Kwak,et al.  More of a Receiver Than a Giver: Why Do People Unfollow in Twitter? , 2012, ICWSM.

[46]  Peng Wang,et al.  Link prediction in social networks: the state-of-the-art , 2014, Science China Information Sciences.