An Inference Framework of Markov Logic Network for Link Prediction in Heterogeneous Networks

Abstract

The presence of multiplex edges and sparse links often hampers the efficacy of link prediction (LP) tasks. By harnessing the expressive power of Markov logic network (MLN) formulations, multiplex edges can be unified to enhance LP effectiveness. However, scaling up inferences for effective LP remains challenging due to the inefficiency of traditional MLN inference methods. To tackle this issue, we redefine LP tasks within heterogeneous networks using MLN inferences and introduce a tailored inference framework to handle unobserved nodes and complex MLN structures. We propose a method to partition the MLN structure into discrete substructures and compute node label distributions using the variational expectation maximization (VEM) algorithm. Additionally, we establish a termination condition to streamline inference search space and present the MLN-based LP algorithm. Experimental findings demonstrate the efficacy of our VEM-driven MLN inference framework for LP tasks in heterogeneous networks, showcasing superior accuracy compared to existing approaches.

Keywords

• heterogeneous network
• link prediction
• Markov logic network
• variational expectation maximization