Log In Sign Up

Node2Seq: Towards Trainable Convolutions in Graph Neural Networks

by   Hao Yuan, et al.

Investigating graph feature learning becomes essentially important with the emergence of graph data in many real-world applications. Several graph neural network approaches are proposed for node feature learning and they generally follow a neighboring information aggregation scheme to learn node features. While great performance has been achieved, the weights learning for different neighboring nodes is still less explored. In this work, we propose a novel graph network layer, known as Node2Seq, to learn node embeddings with explicitly trainable weights for different neighboring nodes. For a target node, our method sorts its neighboring nodes via attention mechanism and then employs 1D convolutional neural networks (CNNs) to enable explicit weights for information aggregation. In addition, we propose to incorporate non-local information for feature learning in an adaptive manner based on the attention scores. Experimental results demonstrate the effectiveness of our proposed Node2Seq layer and show that the proposed adaptively non-local information learning can improve the performance of feature learning.


page 1

page 2

page 3

page 4


Non-Local Graph Neural Networks

Modern graph neural networks (GNNs) learn node embeddings through multil...

Graph Force Learning

Features representation leverages the great power in network analysis ta...

Graph Neural Networks with Composite Kernels

Learning on graph structured data has drawn increasing interest in recen...

Large-Scale Learnable Graph Convolutional Networks

Convolutional neural networks (CNNs) have achieved great success on grid...

Adaptively Connected Neural Networks

This paper presents a novel adaptively connected neural network (ACNet) ...

Network Structure and Feature Learning from Rich but Noisy Data

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

Curvature Graph Neural Network

Graph neural networks (GNNs) have achieved great success in many graph-b...