A Gap in the Community-Size Distribution of a Large-Scale Social Networking Site

Abstract

Social networking sites (SNS) have recently used by millions of people all over the world. An SNS is a society on the Internet, where people communicate and foster friendship with each other. We examine a nation-wide SNS (more than six million users at present), mutually acknowledged friendship network with third million people and nearly two million links. By employing a community-extracting method developed by Newman and others, we found that there exists a range of community-sizes in which only few communities are detected. This novel feature cannot be explained by previous growth models of networks. We present a simple model with two processes of acquaintance, connecting nearest neighbors and random linkage. We show that the model can explain the gap in the community-size distribution as well as other statistical properties including long-tail degree distribution, high transitivity, its correlation with degree, and degree-degree correlation. The model can estimate how the two processes, which are ubiquitous in many social networks, are working with relative frequencies in the SNS as well as other societies.