Evolution of Social-Attribute Networks: Measurements, Modeling, and Implications using Google+

摘要

Understanding social network structure and evolution has importantimplications for many aspects of network and system design includingprovisioning, bootstrapping trust and reputation systems via social networks,and defenses against Sybil attacks. Several recent results suggest thataugmenting the social network structure with user attributes (e.g., location,employer, communities of interest) can provide a more fine-grainedunderstanding of social networks. However, there have been few studies toprovide a systematic understanding of these effects at scale. We bridge thisgap using a unique dataset collected as the Google+ social network grew overtime since its release in late June 2011. We observe novel phenomena withrespect to both standard social network metrics and new attribute-relatedmetrics (that we define). We also observe interesting evolutionary patterns asGoogle+ went from a bootstrap phase to a steady invitation-only stage before apublic release. Based on our empirical observations, we develop a newgenerative model to jointly reproduce the social structure and the nodeattributes. Using theoretical analysis and empirical evaluations, we show thatour model can accurately reproduce the social and attribute structure of realsocial networks. We also demonstrate that our model provides more accuratepredictions for practical application contexts.