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

TL;DR

This paper analyzes the evolution of Google+ social networks by measuring structural and attribute-related properties, developing a generative model, and demonstrating its accuracy in reproducing real-world social and attribute patterns.

Contribution

It provides a systematic large-scale analysis of social-attribute network evolution and introduces a new generative model that accurately captures both social structure and node attributes.

Findings

Novel phenomena in social and attribute metrics observed over time

The generative model accurately reproduces real social network structures

Model improves prediction accuracy for practical applications

Abstract

Understanding social network structure and evolution has important implications for many aspects of network and system design including provisioning, bootstrapping trust and reputation systems via social networks, and defenses against Sybil attacks. Several recent results suggest that augmenting the social network structure with user attributes (e.g., location, employer, communities of interest) can provide a more fine-grained understanding of social networks. However, there have been few studies to provide a systematic understanding of these effects at scale. We bridge this gap using a unique dataset collected as the Google+ social network grew over time since its release in late June 2011. We observe novel phenomena with respect to both standard social network metrics and new attribute-related metrics (that we define). We also observe interesting evolutionary patterns as Google+ went from a bootstrap phase to a steady invitation-only stage before a public release. Based on our empirical observations, we develop a new generative model to jointly reproduce the social structure and the node attributes. Using theoretical analysis and empirical evaluations, we show that our model can accurately reproduce the social and attribute structure of real social networks. We also demonstrate that our model provides more accurate predictions for practical application contexts.