A New Social Network Sampling Algorithm Based on Temperature Conduction Model

A popular solution to dealing with large-scale social networks is to derive a representative sample from a social network. This sample is expected to represent the original social network well such that the sampled network can be used for simulations and analysis. In this paper, we propose a new social network sampling algorithm based on the Temperature Conduction model. Our sampling approach is able to effectively maintain the topological similarity between the sampled network and its original network. We have evaluated our algorithm on several wellknown data sets. The experimental results show that our algorithm outperforms the state-of-the-art methods.