Social Network Sampling

Abstract

In this chapter, we focus on the goal of sampling a representative subgraph as scale-down sampling to addressing the problem of sampling subgraphs from social networks and reviewing recent social network sampling methods in the literature. Then, we introduce four sampling algorithms including DLAS, EDLAS, ICLA-NS and FLAS which utilize learning automata for producing representative subgraphs from online social networks. The DLAS and EDLAS use distributed learning automata and extended distributed learning automata, respectively for both deterministic and stochastic networks. The algorithm ICLA-NS is an extended sampling algorithm with post-processing phase, since it utilizes an irregular cellular learning automaton (ICLA) to guarantee the connectivity and the inclusion of the high degree nodes in subgraphs initially sampled by classic node sampling method. Since most previous studies on sampling from networks either has assumed the network graph is static and fully accessible at any step, or despite considering the stream evolution has not addressed the problem of sampling a representative subgraph from the original graph, the algorithm FLAS as a streaming sampling algorithm based on fixed structure learning automata is introduced with the aim of sampling from activity networks in which the stream of edges continuously evolves over time (i.e. networks are highly dynamic and include a massive volume of edges).