Heterogeneous Temporal Graph Neural Network

by   Yujie Fan, et al.

Graph neural networks (GNNs) have been broadly studied on dynamic graphs for their representation learning, majority of which focus on graphs with homogeneous structures in the spatial domain. However, many real-world graphs - i.e., heterogeneous temporal graphs (HTGs) - evolve dynamically in the context of heterogeneous graph structures. The dynamics associated with heterogeneity have posed new challenges for HTG representation learning. To solve this problem, in this paper, we propose heterogeneous temporal graph neural network (HTGNN) to integrate both spatial and temporal dependencies while preserving the heterogeneity to learn node representations over HTGs. Specifically, in each layer of HTGNN, we propose a hierarchical aggregation mechanism, including intra-relation, inter-relation, and across-time aggregations, to jointly model heterogeneous spatial dependencies and temporal dimensions. To retain the heterogeneity, intra-relation aggregation is first performed over each slice of HTG to attentively aggregate information of neighbors with the same type of relation, and then intra-relation aggregation is exploited to gather information over different types of relations; to handle temporal dependencies, across-time aggregation is conducted to exchange information across different graph slices over the HTG. The proposed HTGNN is a holistic framework tailored heterogeneity with evolution in time and space for HTG representation learning. Extensive experiments are conducted on the HTGs built from different real-world datasets and promising results demonstrate the outstanding performance of HTGNN by comparison with state-of-the-art baselines. Our built HTGs and code have been made publicly accessible at: https://github.com/YesLab-Code/HTGNN.


page 1

page 2

page 3

page 4


Hop-Hop Relation-aware Graph Neural Networks

Graph Neural Networks (GNNs) are widely used in graph representation lea...

Dual Hierarchical Attention Networks for Bi-typed Heterogeneous Graph Learning

Bi-typed heterogeneous graphs are applied in many real-world scenarios. ...

Few-Shot Semantic Relation Prediction across Heterogeneous Graphs

Semantic relation prediction aims to mine the implicit relationships bet...

Heterogeneous Graph Neural Networks for Malicious Account Detection

We present, GEM, the first heterogeneous graph neural network approach f...

HAGNN: Hybrid Aggregation for Heterogeneous Graph Neural Networks

Heterogeneous graph neural networks (GNNs) have been successful in handl...

INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging

Spatio-temporal kriging is an important problem in web and social applic...

Learning Representation over Dynamic Graph using Aggregation-Diffusion Mechanism

Representation learning on graphs that evolve has recently received sign...

Please sign up or login with your details

Forgot password? Click here to reset