Topology-Guided Graph Pre-training and Prompt Learning on Directed Graphs

TMLR Paper6394 Authors

Abstract:

In recent years, graph neural networks (GNNs) have been the dominant approach for graph representation learning, leading to new state-of-the-art
results on many classification and prediction tasks. However, they are limited by the fact that they cannot effectively learn expressive node representations without the guide of labels, thus suffering from the labeled data scarcity problem. To address the challenges of labeling costs and improve robustness in few-shot scenarios, pre-training on self-supervised tasks has garnered significant attention. Additionally, numerous prompting methods have been proposed as effective ways to bridge the gap between pretext tasks and downstream applications. Although graph pre-training and prompt tuning methods have explored various downstream tasks on undirected graphs, directed graphs have been largely under-explored and these models suffer limitations in capture directional and topological information in directed graphs. In this paper, we propose a novel topology-guided directed graph pre-training and prompt tuning model, named TopoDIG, that can effectively capture intrinsic directional structural and local topological features in directed graphs. These features play essential roles in transferring knowledge from a pre-trained model to downstream tasks. For model architecture, TopoDIG consists of an encoder in the form of a magnetic Laplacian matrix, a topological encoder, and a graph prompt learning function. Experimental results on both real-world and synthetic directed graphs demonstrate the superior performance of TopoDIG compared to prominent baseline methods.

Submission Type: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Dmitry_Kobak2

Submission Number: 6394