Publication Type

Journal Article

Version

acceptedVersion

Publication Date

1-2024

Abstract

Graph Neural Networks (GNNs) have become the de facto standard for representation learning on topological graphs, which usually derive effective node representations via message passing from neighborhoods. Although GNNs have achieved great success, previous models are mostly confined to static and homogeneous graphs. However, there are multiple dynamic interactions between different-typed nodes in real-world scenarios like academic networks and e-commerce platforms, forming temporal heterogeneous graphs (THGs). Limited work has been done for representation learning on THGs and the challenges are in two aspects. First, there are abundant dynamic semantics between nodes while traditional techniques like meta-paths can only capture static relevance. Second, different semantics on THGs have often mutually evolved with each other over time, making it more difficult than dynamic homogeneous graph modeling. To address this problem, here we propose the Dynamic Meta-path guided temporal heterogeneous Graph Neural Networks (DyMGNNs). To handle the dynamic semantics, we introduce the concept of dynamic meta-path which is a common base for temporal semantic search engines, and then adopt the temporal importance sampling to extract neighborhoods with temporal bias. Focusing on mutual evolution, we design the heterogeneous mutual evolution attention mechanism, which can model the fine-grained interplay of semanticlevel preferences for each node. Extensive experiments on three real-world datasets for node classification and temporal link prediction demonstrate that our method consistently outperforms state-of-the-art alternatives.

Keywords

Temporal heterogeneous graph, graph neural network, dynamic meta-path, temporal importance sampling, heterogeneous mutual evolution attention

Discipline

Graphics and Human Computer Interfaces | OS and Networks

Research Areas

Data Science and Engineering

Publication

World Scientific Annual Review of Artificial Intelligence

Volume

1

First Page

1

Last Page

22

ISSN

2811-0323

Identifier

10.1142/S2811032323500029

Publisher

World Scientific Publishing Co. Pte. Ltd.

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1142/S2811032323500029

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