Evolution of social-attribute networks: measurements, modeling, and implications using google+

By Neil Zhenqiang Gong, Wenchang Xu, Ling Huang, Prateek Mittal, Emil Stefanov, Vyas Sekar, Dawn Song. 2012

229 citations recorded by the source corpus.

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.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Statistical and Nonlinear Physics

Citations in the corpus

Attacking Graph-based Classification via Manipulating the Graph Structure (2019, methodology): “We obtained an undirected Google+ social network with public user attributes from [26, 35].”
Dependence Makes You Vulnberable: Differential Privacy Under Dependent Tuples (2016, methodology): “3) Degree Distribution: We further consider a graph query whose result is to publish the degree distribution of a large-scale Google+ dataset [19].”
Structural Data De-anonymization: Quantification, Practice, and Implications (2014, background): “Furthermore, let ∆ and ∆ (resp., δ and δ ) be the and second maximum (resp., minimum and second minimum ) degrees of G , respectively.”
On Your Social Network De-anonymizablity: Quantification and Large Scale Evaluation with Seed Knowledge (2015, methodology): “In this section, we quantify the de-anonymizability of Ga in general scenarios, i.e., unlike in Section IV, we assume G(V,E) now could be some graph following an arbitrary network model.”
Big Data: A Survey (2014, background): “Some empirical studies found that proximity bias, geographical limitations, and other factors play important roles in SNS evolution [144–146], and some generation methods are proposed to assist network and system design [147].”
Calibrate: Frequency Estimation and Heavy Hitter Identification with Local Differential Privacy via Incorporating Prior Knowledge (2018, background): “For instance, many realworld phenomena such as video popularity, webpage click frequency, node degrees in social networks–follow power-law distributions [9], [10], [18]; human height follows a Gaussian distribution [2].”
Attribute Inference Attacks in Online Social Networks (2018, background): “Existing network generators (e.g., preferential attachment model [4] and SAN model [24]) cannot generate SBA networks.”
Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation (2018, methodology): “We obtained an undirected Google+ social network with user attributes from [35], [53].”
Big data related technologies, challenges and future prospects (2015, background): “In [43], the problem of cleaning RFID data was examined.”
SybilBelief: A Semi-Supervised Learning Approach for Structure-Based Sybil Detection (2013, methodology): “For SB-B, we randomly sample 100 nodes from the entire network including both benign and Sybil regions and treat them as Sybil labels.”
Joint Link Prediction and Attribute Inference Using a Social-Attribute Network (2014, methodology): “We use Employment and Education to construct a vocabulary of attributes in this paper because previous work [Gong et al. 2012b] showed that these two attribute”
SybilFuse: Combining Local Attributes with Global Structure to Perform Robust Sybil Detection (2018, background): “Furthermore, we ﬁnd that the number of neighbors of both benign and Sybil nodes follow long-tail distributions, which is also widely observed in other OSNs such as LiveJournal [41] and Google+ [42].”
AttriInfer: Inferring User Attributes in Online Social Networks Using Markov Random Fields (2017, methodology): “, around 70% of Google+ users did not disclose any attributes [14]), and these users are potential target users.”
Toward Scalable Systems for Big Data Analytics: A Technology Tutorial (2014, background): “Several empirical studies [234]– [236] have found that proximity bias, geographic constraints, and certain other factors play an important role in the evolution of social networks, and several generative models [237] have been proposed to assist the network and system design.”