Multi-hop Convolutions on Weighted Graphs

TL;DR

This paper introduces a multi-hop convolutional network for weighted graphs that efficiently captures multi-scale information without increasing complexity or redundant data, outperforming existing methods in classification tasks.

Contribution

The paper proposes a novel multi-hop convolutional approach with adaptive weighting for weighted graphs, reducing complexity and redundancy while enhancing performance.

Findings

Achieves highest accuracy on six benchmark datasets

Outperforms state-of-the-art graph convolution methods

Effectively captures multi-scale contextual information

Abstract

Graph Convolutional Networks (GCNs) have made significant advances in semi-supervised learning, especially for classification tasks. However, existing GCN based methods have two main drawbacks. First, to increase the receptive field and improve the representation capability of GCNs, larger kernels or deeper network architectures are used, which greatly increases the computational complexity and the number of parameters. Second, methods working on higher order graphs computed directly from adjacency matrices may alter the relationship between graph nodes, particularly for weighted graphs. In addition, the direct construction of higher-order graphs introduces redundant information, which may result in lower network performance. To address the above weaknesses, in this paper, we propose a new method of multi-hop convolutional network on weighted graphs. The proposed method consists of multiple convolutional branches, where each branch extracts node representation from a $k$-hop graph with small kernels. Such design systematically aggregates multi-scale contextual information without adding redundant information. Furthermore, to efficiently combine the extracted information from the multi-hop branches, an adaptive weight computation (AWC) layer is proposed. We demonstrate the superiority of our MultiHop in six publicly available datasets, including three citation network datasets and three medical image datasets. The experimental results show that our proposed MultiHop method achieves the highest classification accuracy and outperforms the state-of-the-art methods. The source code of this work have been released on GitHub (https://github.com/ahukui/Multi-hop-Convolutions-on-Weighted-Graphs).