Reliable Link Inference for Network Data With Community Structures.

Abstract

Complex systems are often characterized by complex networks with links and entities. However, in many complex systems such as protein-protein interaction networks, recommender systems, and online communities, their links are hard to reveal directly, but they can be inaccurately observed by multiple data collection platforms or by a data collection platform at different times. Then, the links of the systems are inferred by the integration of the collected observations. As those data collection platforms are usually distributed over a large area and in different fields, their observations are unreliable and sensitive to the potential structures of the systems. In this paper, we consider the link inference problem in network data with community structures, in which the reliability of data collection platforms is unknown a priori and the link errors and reliability of platforms' observations are heterogeneous to the underlying community structures of the systems. We propose an expectation maximization algorithm for link inference in a network system with community structures (EMLIC). The EMLIC algorithm is also used to infer the link errors and reliability of platforms' observations in different communities. Experimental results on both synthetic data and eight real-world network data demonstrate that our algorithm is able to achieve lower link errors than the existing reliable link inference algorithms when the network data have community structures.