Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

3-2023

Abstract

Graph-structured data widely exist in diverse real-world scenarios, analysis of these graphs can uncover valuable insights about their respective application domains. However, most previous works focused on learning node representation from a single fixed graph, while many real-world scenarios require representations to be quickly generated for unseen nodes, new edges, or entirely new graphs. This inductive ability is essential for high-throughtput machine learning systems. However, this inductive graph representation problem is quite difficult, compared to the transductive setting, for that generalizing to unseen nodes requires new subgraphs containing the new nodes to be aligned to the neural network trained already. Meanwhile, following a message passing framework, graphneural network (GNN) is an inductive and powerful graph representation tool. We further explore inductive GNN from more specific perspectives: (1) generalizing GNN across graphs, in which we tackle with the problem of semi-supervised node classification across graphs; (2) generalizing GNN across time, in which we mainly solve the problem of temporal link prediction; (3) generalizing GNN across tasks; (4) generalizing GNN across locations.

Keywords

Graph neural network, graph-structured data, inductive

Discipline

Databases and Information Systems | OS and Networks

Publication

WSDM 2023: Proceedings of the 16th ACM International Conference on Web Search and Data Mining: Singapore, February 27 - March 3

First Page

1214

Last Page

1215

ISBN

9781450394079

Identifier

10.1145/3539597.3572986

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/3539597.3572986

Download

Share

COinS