基于多通道的边学习图卷积网络

doi:10.11959/j.issn.1000-0801.2022250

Abstract

Abstract:

Usually the edges of the graph contain important information of the graph.However, most of deep learning models for graph learning, such as graph convolutional network (GCN) and graph attention network (GAT), do not fully utilize the characteristics of multi-dimensional edge features.Another problem is that there may be noise in the graph that affects the performance of graph learning.Multilayer perceptron (MLP) was used to denoise and optimize the graph data, and a multi-channel learning edge feature method was introduced on the basis of GCN.The multi-dimensional edge attributes of the graph were encoded, and the attributes contained in the original graph were modeled as multi-channel.Each channel corresponds to an edge feature attribute to constrain the training of graph nodes, which allows the algorithm to learn multi-dimensional edge features in the graph more reasonably.Experiments based on Cora, Tox21, Freesolv and other datasets had proved the effectiveness of denoising methods and multi-channel methods.

Key words: graph convolutional network, edge feature, graph denoising, multi-channel, edge-learning

CLC Number:

TP391

Shuai YANG, Ruiqin WANG, Hui MA. Multi-channel based edge-learning graph convolutional network[J]. Telecommunications Science, 2022, 38(9): 95-104.

Figures/Tables 9

模型	GCN	GAT	EGCN
时间复杂度	$O (K m d + K n d^{2})$	$O (K m d + K n d^{2})$	$O (c K m d + c K n d^{2})$
内存复杂度	$O (K n d + K d^{2})$	$O (K n d + K d^{2})$	$O (c K n d + c K d^{2})$

模型	Cora		Citeseer
模型	Sparse	Dense	Sparse	Dense
GCN	0.729±0.008	0.720±1.002	0.692±0.007	0.753±0.004
GAT	0.755±1.001	0.790±1.000	0.695±0.005	0.749±0.005
EGNN	0.834±0.003	$0 . 888 \pm 0 . 003$	0.706±0.003	0.771±0.004
EGCN	$0 . 849 \pm 0 . 005$	0.883±0.005	$0 . 747 \pm 0 . 009$	$0 . 791 \pm 0 . 004$

模型	Cora		Citeseer
模型	Sparse	Dense	Sparse	Dense
EGCN(N)	0.827±0.004	0.861±0.005	0.725±0.002	0.74.9±0.005
EGCN(M)	0.781±0.004	0.802±0.004	0.698±0.004	0.78.7±0.003
EGCN(MN)	$0 . 849 \pm 0 . 005$	$0 . 883 \pm 0 . 005$	$0 . 747 \pm 0 . 009$	$0 . 791 \pm 0 . 004$

模型	Tox21（AUC-ROC）		Lipo（RMSE）		Freesolv（RMSE）		eSOL（RMSE）
模型	val	test	val	test	val	test	val	test
RF	0.78±0.01	0.75±0.02	0.87±0.02	0.86±0.03	1.98±0.82	1.62±0.14	1.27±0.07	1.18±0.11
Weave	0.79±0.02	0.80±0.01	0.88±0.11	0.89±0.09	1.35±0.25	1.37±0.19	0.90±0.08	0.85±0.03
GCN	0.82±0.01	0.83±0.02	0.70±0.03	0.68±0.03	1.24±0.26	1.14±0.10	0.76±0.08	0.80±0.17
GAT	0.83±0.02	0.83±0.01	0.85±0.02	0.84±0.01	1.73±0.25	1.45±0.25	0.86±0.02	1.01±0.03
MPNN	—	—	0.70±0.04	0.68±0.04	1.38±0.42	1.40±0.08	0.78±0.07	0.80±0.06
EIGNN	—	—	$0 . 63 \pm 0 . 02$	$0 . 65 \pm 0 . 02$	1.24±0.13	1.27±0.14	0.75±0.08	0.77±0.07
EGNN	0.82±0.01	0.82±0.01	0.79±0.02	0.75±0.01	1.09±0.08	1.01±0.12	0.70±0.03	0.72±0.02
EGCN	$0 . 85 \pm 0 . 01$	$0 . 86 \pm 0 . 01$	0.66±0.08	0.65±0.04	$0 . 99 \pm 0 . 12$	$0 . 97 \pm 0 . 13$	$0 . 63 \pm 0 . 06$	$0 . 69 \pm 0 . 10$

模型	Freesolv		eSOL
模型	val	test	val	test
EGCN₅	$0 . 99 \pm 0 . 13$	$0 . 97 \pm 0 . 12$	$0 . 65 \pm 0 . 06$	$0 . 68 \pm 0 . 07$
EGCN₄	1.08±0.16	1.04±0.14	0.65±0.06	0.69±0.08
EGCN₃	1.11±0.08	1.10±0.13	0.67±0.07	0.71±0.04
EGCN₂	1.16±0.18	1.22±0.09	0.68±0.08	0.73±0.07
GCN	1.24±0.26	1.14±0.10	0.76±0.08	0.80±0.17

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数据集	节点数（分子数）/个	边/个	标签数/个	任务类型
Cora	2 708	5 429	7	分类
CiteSeer	3 327	4 732	6	分类
Tox21	7 433	—	13	分类
Freesolv	642	—	2	回归
eSOL	1 125	—	2	回归
Lipo	4 190	—	9	回归

Multi-channel based edge-learning graph convolutional network

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