Communication structure discovery via information asymmetry in an organizational social network

In an organization, based on the positions of employees there is usually an existing hierarchy among them. However, in real-life cases, people's interactions tend to form a certain communication structure due to some external forces or personal factors. In this paper, we aim at discovering the potential communication structure, in which nodes are typed labels (e.g. job-titles) and edges stand for tight interactions between typed labels in an organizational social network. To tackle this problem, we propose to exploit the concept of information asymmetry to model the core-periphery property in the communication structure. The proximity asymmetry is defined to realize the information asymmetry. We also devise two random-walk methods to calculate the proximity asymmetry between typed labels. The experiments conducted on the Enron email dataset shows that the proposed method outperforms some heuristic ones.