Efficient Search in Networks Using Conductance

Decentralized search in networks is an important algorithmic problem in the study of complex networks and social networks analysis. It has a large number of practical applications, from shortest paths search in social network relationship, web pages search in WWW to querying files in peer-to-peer file sharing networks and so on. In this paper, we explore this problem from a perspective of community structure. We first find that through maximizing sample conductance, we can get high coverage sample. Based on this result, then, we propose a new decentralized search strategy named Conductance Search which tries to efficiently find the nodes belonging to different communities. We compare the strategy with other common strategies. And the results show that the conductance search outperforms others in number of steps to find the target and time complexity. Finally, we find some previous conclusions fail in many real-world networks and discuss network search-ability from the perspective of various structural properties.