Variational Gridded Graph Convolution Network for Node Classification  

作　　者：Xiaobin Hong Tong Zhang Zhen Cui Jian Yang 

机构地区：[1]Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education,School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2021年第10期1697-1708,共12页自动化学报（英文版）

基　　金：supported by the Natural Science Foundation of Jiangsu Province(BK20190019,BK20190452);the National Natural Science Foundation of China(62072244,61906094);the Natural Science Foundation of Shandong Province(ZR2020LZH008)。

摘　　要：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 hcrwalk 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 randomsampling 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.

关 键 词：Graph coarsening GRIDDING node classification random walk variational convolution 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]