Evolutionary structure learning on temporal networks using von Neumann entropy

Abstract

Temporal networks effectively represent diverse real-world dynamic systems, but their evolving nature poses challenges in developing robust models. Message-passing mechanisms in these models face increasing computational complexity as nodes process expanding neighborhoods over time. To address this issue, we introduce von Neumann entropy, an effective graph representation of static graph structure. By approximately computing von Neumann entropy on the temporal network, an Evolutionary Structure Aware Network (ESAN) framework is proposed for evolutionary structure recognition. ESAN leverages von Neumann entropy to identify and emphasize key structural changes, enabling insightful analysis of network evolution. Specifically, ESAN employs an evolutionary structure importance sampling algorithm to capture evolution laws by measuring von Neumann entropy changes. Relative structure information encoding further enhances edge structural information. Extensive evaluations on transductive and inductive link prediction tasks demonstrate the superiority of ESAN against state-of-the-art baselines.