Sampling social networks using shortest paths

In recent years, online social networks (OSN) have emerged as a platform of sharing variety of information about people, and their interests, activities, events and news from real worlds. Due to the large scale and access limitations (e.g., privacy policies) of online social network services such as Facebook and Twitter, it is difficult to access the whole public network in a limited amount of time. For this reason researchers try to study and characterize OSN by taking appropriate and reliable samples from the network. In this paper, we propose to use the concept of shortest path for sampling social networks. The proposed sampling method first finds the shortest paths between several pairs of nodes selected according to some criteria. Then the edges in these shortest paths are ranked according to the number of times that each edge has appeared in the set of found shortest paths. The sampled network is then computed as a subgraph of the social network which contains a percentage of highly ranked edges. In order to investigate the performance of the proposed sampling method, we provide a number of experiments on synthetic and real networks. Experimental results show that the proposed sampling method outperforms the existing method such as random edge sampling, random node sampling, random walk sampling and Metropolis-Hastings random walk sampling in terms of relative error (RE), normalized root mean square error (NMSE), and Kolmogorov-Smirnov (KS) test.