Enhancing Cross-domain Link Prediction via Evolution Process Modeling

Xuanwen Huang, Wei Chow, Yize Zhu, YANG WANG, Ziwei Chai, Chunping Wang, Lei CHEN, Yang Yang

Track: Graph algorithms and modeling for the Web

Keywords: Dynamic Graph, Link prerdiction

TL;DR: We propose CrossLink, a novel framework for cross-domain link prediction.

Abstract:

This paper proposes CrossLink, a novel framework for cross-domain link prediction.
CrossLink learns the evolution pattern of a specific downstream graph and subsequently makes pattern-specific link predictions.
It employs a technique called \textit{conditioned link generation}, which integrates both evolution and structure modeling to perform evolution-specific link prediction.
This conditioned link generation is carried out by a transformer-decoder architecture, enabling efficient parallel training and inference.
CrossLink is trained on extensive dynamic graphs across diverse domains, encompassing 6 million dynamic edges.
Extensive experiments on eight untrained graphs demonstrate that CrossLink achieves state-of-the-art performance in cross-domain link prediction.
Compared to advanced baselines under the same settings, CrossLink shows an average improvement of 11.40% in Average Precision across eight graphs. Impressively, it surpasses the fully supervised performance of 8 advanced baselines on 6 untrained graphs.

Submission Number: 36