Schema-Aware Deep Graph Convolutional Networks for Heterogeneous Graphs

by   Saurav Manchanda, et al.

Graph convolutional network (GCN) based approaches have achieved significant progress for solving complex, graph-structured problems. GCNs incorporate the graph structure information and the node (or edge) features through message passing and computes 'deep' node representations. Despite significant progress in the field, designing GCN architectures for heterogeneous graphs still remains an open challenge. Due to the schema of a heterogeneous graph, useful information may reside multiple hops away. A key question is how to perform message passing to incorporate information of neighbors multiple hops away while avoiding the well-known over-smoothing problem in GCNs. To address this question, we propose our GCN framework 'Deep Heterogeneous Graph Convolutional Network (DHGCN)', which takes advantage of the schema of a heterogeneous graph and uses a hierarchical approach to effectively utilize information many hops away. It first computes representations of the target nodes based on their 'schema-derived ego-network' (SEN). It then links the nodes of the same type with various pre-defined metapaths and performs message passing along these links to compute final node representations. Our design choices naturally capture the way a heterogeneous graph is generated from the schema. The experimental results on real and synthetic datasets corroborate the design choice and illustrate the performance gains relative to competing alternatives.


page 1

page 2

page 3

page 4


Node Feature Kernels Increase Graph Convolutional Network Robustness

The robustness of the much-used Graph Convolutional Networks (GCNs) to p...

Ordered GNN: Ordering Message Passing to Deal with Heterophily and Over-smoothing

Most graph neural networks follow the message passing mechanism. However...

Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples

Graph Convolutional Neural Networks (GCNs) has been generally accepted t...

Adaptive Propagation Graph Convolutional Network

Graph convolutional networks (GCNs) are a family of neural network model...

UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation

With the recent success of graph convolutional networks (GCNs), they hav...

RIM: Reliable Influence-based Active Learning on Graphs

Message passing is the core of most graph models such as Graph Convoluti...

Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks

Graph convolutional networks (GCNs) have shown promising results in proc...

Please sign up or login with your details

Forgot password? Click here to reset