Social signature identification of dynamical social networks

Abstract Understanding the social relation of dynamical online social networks (OSNs) is significant for identifying the strong and weak ties. In this paper, we empirically investigate the evolution characteristics of Facebook and Wiki users’ social signature, capturing the distribution of frequency of interactions between different alters over time in ego network. The statistical results show that there are robust social signatures for collective egos based on interactions. Notably, individual social signature remains stable, no matter how alters change over time. Furthermore, we use structure information of OSNs to predict the social closeness in each interval in terms of embeddedness measurement, and find that alters identified from structural information still show the characteristic of social signature. The accuracy of the prediction in terms of embeddedness ranges from 33.47% to 66.90%. This work enables an effective method for identifying potential highly-related friends based on the regularity of social signature in online social network.

[1]  Lei Hou,et al.  Identifying the Role of Common Interests in Online User Trust Formation , 2015, PloS one.

[2]  Cameron Marlow,et al.  A 61-million-person experiment in social influence and political mobilization , 2012, Nature.

[3]  G. A. Miller.  THE PSYCHOLOGICAL REVIEW THE MAGICAL NUMBER SEVEN, PLUS OR MINUS TWO: SOME LIMITS ON OUR CAPACITY FOR PROCESSING INFORMATION 1 , 1956 .

[4]  Zhi-Dan Zhao,et al.  Dynamic Patterns of Academic Forum Activities , 2015, ArXiv.

[5]  Daniel J. Brass,et al.  Network Analysis in the Social Sciences , 2009, Science.

[6]  E. Todeva.  Networks , 2007 .

[7]  Jure Leskovec,et al.  Governance in Social Media: A Case Study of the Wikipedia Promotion Process , 2010, ICWSM.

[8]  E. David,et al.  Networks, Crowds, and Markets: Reasoning about a Highly Connected World , 2010 .

[9]  Zhi-Dan Zhao,et al.  Non-Markovian Character in Human Mobility: Online and Offline , 2014, Chaos.

[10]  Yi-Cheng Zhang,et al.  Solving the apparent diversity-accuracy dilemma of recommender systems , 2008, Proceedings of the National Academy of Sciences.

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

[12]  Dylan Walker,et al.  Tie Strength, Embeddedness, and Social Influence: A Large-Scale Networked Experiment , 2014, Manag. Sci..

[13]  Jian-Guo Liu,et al.  Evolution properties of online user preference diversity , 2017 .

[14]  Bernardo A. Huberman,et al.  Rhythms of social interaction: messaging within a massive online network , 2006, ArXiv.

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

[16]  Jari Saramäki,et al.  Personality traits and ego-network dynamics , 2017, PloS one.

[17]  Xiaofan Wang,et al.  Generalized local-world models for weighted networks. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[18]  R I M Dunbar,et al.  Do online social media cut through the constraints that limit the size of offline social networks? , 2016, Royal Society Open Science.

[19]  Jon M. Kleinberg,et al.  Romantic partnerships and the dispersion of social ties: a network analysis of relationship status on facebook , 2013, CSCW.

[20]  Robin I. M. Dunbar,et al.  Neocortex size predicts group size in carnivores and some insectivores , 1998 .

[21]  H. Russell Bernard,et al.  On the social structure of an ocean-going research vessel and other important things , 1973 .

[22]  Lin Zhao,et al.  Evolutionary games on scale-free networks with a preferential selection mechanism , 2009 .

[23]  Robin I. M. Dunbar,et al.  Understanding primate brain evolution , 2007, Philosophical Transactions of the Royal Society B: Biological Sciences.

[24]  Robin I. M. Dunbar,et al.  Social network size in humans , 2003, Human nature.

[25]  Marco Conti,et al.  The structure of online social networks mirrors those in the offline world , 2015, Soc. Networks.

[26]  Damon Centola,et al.  The Spread of Behavior in an Online Social Network Experiment , 2010, Science.

[27]  James Stiller,et al.  Perspective-taking and memory capacity predict social network size , 2007, Soc. Networks.

[28]  Jari Saramäki,et al.  Dynamics of close relationships for the life-course migration , 2014, arXiv.org.

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

[30]  Jure Leskovec,et al.  Motifs in Temporal Networks , 2016, WSDM.

[31]  Matthijs Kalmijn,et al.  Shared friendship networks and the life course: an analysis of survey data on married and cohabiting couples , 2003, Soc. Networks.

[32]  Qiang Guo,et al.  Evolution Characteristics of the Network Core in the Facebook , 2014, PloS one.

[33]  Mark S. Granovetter,et al.  Getting a Job: A Study of Contacts and Careers. , 1976 .

[34]  Robin I. M. Dunbar,et al.  Evolution of the Social Brain , 2003, Science.

[35]  Qiang Guo,et al.  Effect of the time window on the heat-conduction information filtering model , 2014 .

[36]  Robin I. M. Dunbar,et al.  Neocortex size and social network size in primates , 2001, Animal Behaviour.

[37]  Wenxu Wang,et al.  Mutual selection model for weighted networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[38]  Robin I. M. Dunbar.  Coevolution of neocortical size, group size and language in humans , 1993, Behavioral and Brain Sciences.

[39]  P. V. Marsden,et al.  Measuring Tie Strength , 1984 .

[40]  Zhao-Long Hu,et al.  Statistical properties of the personal social network in the Facebook , 2013 .

[41]  Sinan Aral,et al.  Identifying Influential and Susceptible Members of Social Networks , 2012, Science.

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

[43]  Romualdo Pastor-Satorras,et al.  Model reproduces individual, group and collective dynamics of human contact networks , 2014, Soc. Networks.

[44]  Alessandro Vespignani,et al.  Modeling Users' Activity on Twitter Networks: Validation of Dunbar's Number , 2011, PloS one.

[45]  Diane Felmlee,et al.  No Couple Is an Island: A Social Network Perspective on Dyadic Stability , 2001 .

[46]  Qiang Guo,et al.  Memory effect of the online user preference , 2014, Scientific Reports.

[47]  Robin I. M. Dunbar,et al.  Orbital prefrontal cortex volume predicts social network size: an imaging study of individual differences in humans , 2012, Proceedings of the Royal Society B: Biological Sciences.

[48]  Boleslaw K. Szymanski,et al.  Quantifying patterns of research-interest evolution , 2017, Nature Human Behaviour.