Lingfei’s Study on the Growth of Online Communities Published on Physical Review E

Lingfei’s Study on the Growth of Online Communities Published on Physical Review E

Lingfei Wu, one of the lab members and Jiang Zhang, a researcher from Beijing Normal University, collaboratively investigated the growth pattern of online communities and found that the growth rate can be predicted by the distribution of individual activities. Their paper, titled Accelerating Growth and Size-dependent Distribution of Human Online Activities, is published on Physical Review E.

Wu and Zhang suggested that previous research on human online activities usually assumes that total activity T increases linearly with active population P, that is, T∝P^γ (γ=1). However, they found examples of online communities including micro-blogging sites (Jiwai), news voting sites (Digg), and photo tagging sites (Flickr and Delicious) where total activity grows faster than active population, which can be expressed as T∝P^γ (γ>1). They called this pattern “accelerating growth” and found it related to a type of distribution that changes with system size. They showed both analytically and empirically how the growth rate γ can be predicted from a scaling parameter b in the size-dependent distribution. As previous studies suggested that real-world complex systems such as cities and biological networks also exhibited similar growth pattern as the one discovered in the virtual world, Wu and Zhang proposed that their model can be used to explain the regularity governing the scaling up of both real-life systems and virtual communities.