Efficient Link Prediction in Continuous-Time Dynamic Networks using Optimal Transmission and Metropolis Hastings Sampling

TL;DR

This paper introduces a novel framework for link prediction in continuous-time dynamic networks that combines optimal transmission theory with Metropolis Hastings sampling to reduce information loss and better capture complex structural relationships.

Contribution

The paper proposes a new method integrating optimal transmission and Metropolis Hastings sampling for improved link prediction in continuous-time dynamic networks, addressing limitations of previous approaches.

Findings

Outperforms existing methods on multiple datasets

Effectively captures higher-order structural relationships

Reduces information loss in node information propagation

Abstract

Efficient link prediction in continuous-time dynamic networks is a challenging problem that has attracted much research attention in recent years. A widely used approach to dynamic network link prediction is to extract the local structure of the target link through temporal random walk on the network and learn node features using a coding model. However, this approach often assumes that candidate temporal neighbors follow some certain types of distributions, which may be inappropriate for real-world networks, thereby incurring information loss. To address this limitation, we propose a framework in continuous-time dynamic networks based on Optimal Transmission (OT) and Metropolis Hastings (MH) sampling (COM). Specifically, we use optimal transmission theory to calculate the Wasserstein distance between the current node and the time-valid candidate neighbors to minimize information loss in node information propagation. Additionally, we employ the MH algorithm to obtain higher-order structural relationships in the vicinity of the target link, as it is a Markov Chain Monte Carlo method and can flexibly simulate target distributions with complex patterns. We demonstrate the effectiveness of our proposed method through experiments on eight datasets from different fields.