Variational Gridded Graph Convolution Network for Node Classification
Xiaobin Hong, Tong Zhang, Zhen Cui, Jian Yang
Abstract
The existing graph convolution methods usually suffer high computational burdens, large memory requirements, and intractable batch-processing. In this paper, we propose a high-efficient variational gridded graph convolution network (VG-GCN) to encode non-regular graph data, which overcomes all these aforementioned problems. To capture graph topology structures efficiently, in the proposed framework, we propose a hierarchically-coarsened random walk (hcr-walk) by taking advantage of the classic random walk and node/edge encapsulation. The hcr-walk greatly mitigates the problem of exponentially explosive sampling times which occur in the classic version, while preserving graph structures well. To efficiently encode local hcr-walk around one reference node, we project hcr-walk into an ordered space to form image-like grid data, which favors those conventional convolution networks. Instead of the direct 2-D convolution filtering, a variational convolution block (VCB) is designed to model the distribution of the random-sampling hcr-walk inspired by the well-formulated variational inference. We experimentally validate the efficiency and effectiveness of our proposed VG-GCN, which has high computation speed, and the comparable or even better performance when compared with baseline GCNs.