Sharing Social Network Data: Differentially Private Estimation of Exponential-Family Random Graph Models

Motivated by a real-life problem of sharing social network data that contain sensitive personal information, we propose a novel approach to release and analyze synthetic graphs in order to protect privacy of individual relationships captured by the social network while maintaining the validity of statistical results. A case study using a version of the Enron e-mail corpus dataset demonstrates the application and usefulness of the proposed techniques in solving the challenging problem of maintaining privacy \emph{and} supporting open access to network data to ensure reproducibility of existing studies and discovering new scientific insights that can be obtained by analyzing such data. We use a simple yet effective randomized response mechanism to generate synthetic networks under $\epsilon$-edge differential privacy, and then use likelihood based inference for missing data and Markov chain Monte Carlo techniques to fit exponential-family random graph models to the generated synthetic networks.Comment: Updated, 39 page

Follow Whom? Chinese Users Have Different Choice

Sina Weibo, which was launched in 2009, is the most popular Chinese micro-blogging service. It has been reported that Sina Weibo has more than 400 million registered users by the end of the third quarter in 2012. Sina Weibo and Twitter have a lot in common, however, in terms of the following preference, Sina Weibo users, most of whom are Chinese, behave differently compared with those of Twitter. This work is based on a data set of Sina Weibo which contains 80.8 million users' profiles and 7.2 billion relations and a large data set of Twitter. Firstly some basic features of Sina Weibo and Twitter are analyzed such as degree and activeness distribution, correlation between degree and activeness, and the degree of separation. Then the following preference is investigated by studying the assortative mixing, friend similarities, following distribution, edge balance ratio, and ranking correlation, where edge balance ratio is newly proposed to measure balance property of graphs. It is found that Sina Weibo has a lower reciprocity rate, more positive balanced relations and is more disassortative. Coinciding with Asian traditional culture, the following preference of Sina Weibo users is more concentrated and hierarchical: they are more likely to follow people at higher or the same social levels and less likely to follow people lower than themselves. In contrast, the same kind of following preference is weaker in Twitter. Twitter users are open as they follow people from levels, which accords with its global characteristic and the prevalence of western civilization. The message forwarding behavior is studied by displaying the propagation levels, delays, and critical users. The following preference derives from not only the usage habits but also underlying reasons such as personalities and social moralities that is worthy of future research.Comment: 9 pages, 13 figure

Discovering Determinants of Project Participation in an Open Source Social Network

Successful open source software projects often require a steady supply of self motivated software developers. However, little work has been done from a relational/network perspective to study the factors that drive the developers to participate in OSS projects. In this paper, we investigate the participation dynamics in a social network, particularly in an online open source community called Ohloh. Through a REST-based API, we collected information about 11,530 open source software projects involving 94,330 developers. Using social network analysis and statistical analysis methods, we examine a set of social and technical factors in the Ohloh dataset, which we define as the determinants that significantly influence the developers’ participation choices. We found that the determinants include (1)homophily in programming language, (2)project mutual acquaintance, and (3)project age. In addition, our research findings provide the possibility of predicting developers’ participation choices based on the discovered determinants, and therefore can have important implications for OSS project management and in designing social network enabled recommendation systems

School ties: An analysis of homophily in an adolescent friendship network

Homophily is the tendency to establish relationships among people who share similar characteristics or attributes. This study presents evidence of homophilic behaviour for an adolescent friendship network of 6,961 links in the West of England. We control for unobserved characteristics by estimating school and individual fixed effects and present evidence on the role of length and closeness of friendships on the degree of homophily. We also exploit the dynamics of the friendship by comparing similarities among existing and future friends. Results indicate that academic achievement, personality, educational aspirations, bad behaviour and mother’s education are essential in the friendship formation process. However, income and parents’ occupational class proved to be insignificant. We also show that the degree of homophily among friends selected from a random process is much lower than that of the observed friendships.Networks, Homophily, Segregation, Friendships, Adolescents