Log In Sign Up

Meta Graph Attention on Heterogeneous Graph with Node-Edge Co-evolution

by   Yucheng Lin, et al.

Graph neural networks have become an important tool for modeling structured data. In many real-world systems, intricate hidden information may exist, e.g., heterogeneity in nodes/edges, static node/edge attributes, and spatiotemporal node/edge features. However, most existing methods only take part of the information into consideration. In this paper, we present the Co-evolved Meta Graph Neural Network (CoMGNN), which applies meta graph attention to heterogeneous graphs with co-evolution of node and edge states. We further propose a spatiotemporal adaption of CoMGNN (ST-CoMGNN) for modeling spatiotemporal patterns on nodes and edges. We conduct experiments on two large-scale real-world datasets. Experimental results show that our models significantly outperform the state-of-the-art methods, demonstrating the effectiveness of encoding diverse information from different aspects.


page 1

page 2

page 3

page 4


Heterogeneous Graph Tree Networks

Heterogeneous graph neural networks (HGNNs) have attracted increasing re...

Can Graph Neural Networks Go "Online"? An Analysis of Pretraining and Inference

Large-scale graph data in real-world applications is often not static bu...

SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks

Understanding traffic scenes requires considering heterogeneous informat...

Exploring Edge Disentanglement for Node Classification

Edges in real-world graphs are typically formed by a variety of factors ...

Network Structure and Feature Learning from Rich but Noisy Data

In the study of network structures, much attention has been devoted to n...

XG-BoT: An Explainable Deep Graph Neural Network for Botnet Detection and Forensics

In this paper, we proposed XG-BoT, an explainable deep graph neural netw...

Deep Multi-attributed Graph Translation with Node-Edge Co-evolution

Generalized from image and language translation, graph translation aims ...