PAFit: An R Package for Modeling and Estimating Preferential Attachment and Node Fitness in Temporal Complex Networks

Many real-world systems are profitably described as complex networks that grow over time. Preferential attachment and node fitness are two ubiquitous growth mechanisms that not only explain certain structural properties commonly observed in real-world systems, but are also tied to a number of applications in modeling and inference. While there are standard statistical packages for estimating the structural properties of complex networks, there is no corresponding package when it comes to the estimation of growth mechanisms. This paper introduces the R package PAFit, which implements well-established statistical methods for estimating preferential attachment and node fitness, as well as a number of functions for generating complex networks from these two mechanisms. The main computational part of the package is implemented in C++ with OpenMP to ensure good performance for large-scale networks. In this paper, we first introduce the main functionalities of PAFit using simulated examples, and then use the package to analyze a collaboration network between scientists in the field of complex networks.


PAFit: An R Package for Modeling and Estimating Preferential Attachment and Node Fitness in Temporal Complex Networks
Thong Pham, Paul Sheridan, Hidetoshi Shimodaira


Socinfo2017 – 9th International Conference on Social Informatics

We are delighted to welcome the 9th International Conference on Social Informatics (SocInfo 2017) to Oxford, UK, in September 2017.

SocInfo is an interdisciplinary venue for researchers from Computer Science, Informatics, Social Sciences and Management Sciences to share ideas and opinions, and present original research work on studying the interplay between socially-centric platforms and social phenomena.
The ultimate goal of Social Informatics is to create better understanding of socially-centric platforms not just as a technology, but also as a set of social phenomena. To that end, we are inviting interdisciplinary papers, on applying information technology in the study of social phenomena, on applying social concepts in the design of information systems, on applying methods from the social sciences in the study of social computing and information systems, on applying computational algorithms to facilitate the study of social systems and human social dynamics, and on designing information and communication technologies that consider social context.


Exercise contagion in a global social network

We leveraged exogenous variation in weather patterns across geographies to identify social contagion in exercise behaviours across a global social network. We estimated these contagion effects by combining daily global weather data, which creates exogenous variation in running among friends, with data on the network ties and daily exercise patterns of ∼1.1M individuals who ran over 350M km in a global social network over 5 years. Here we show that exercise is socially contagious and that its contagiousness varies with the relative activity of and gender relationships between friends. Less active runners influence more active runners, but not the reverse. Both men and women influence men, while only women influence other women. While the Embeddedness and Structural Diversity theories of social contagion explain the influence effects we observe, the Complex Contagion theory does not. These results suggest interventions that account for social contagion will spread behaviour change more effectively.


Exercise contagion in a global social network
Sinan Aral & Christos Nicolaides
Nature Communications 8, Article number: 14753 (2017)


Marching for the Right to Be Wrong

Government, where decisions made in a moment can affect millions of people for a lifetime, needs constant reminders of its fallibility. A big part of that has to be a proper respect for the methods of science, as well as for its substantive discoveries. Psychologists assure us that human beings have a strong desire to accept things as true because we want them to be true, not only because they are the best explanation for what we observe. In the hands of policy-makers, that natural tendency can have deadly consequences. Science has developed impressive (though not infallible) techniques for correcting for such biases; our government could stand to do a bit better.