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《山东大学学报(理学版)》 ›› 2024, Vol. 59 ›› Issue (7): 85-94.doi: 10.6040/j.issn.1671-9352.1.2023.064

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面向图神经网络表征学习的类别知识探针

黄兴宇1,2(),赵明宇1,3,吕子钰1,2,*()   

  1. 1. 中国科学院深圳先进技术研究院,广东 深圳 518055
    2. 中山大学网络空间安全学院,广东 深圳 518107
    3. 中国科学院大学,北京 100101
  • 收稿日期:2023-11-24 出版日期:2024-07-20 发布日期:2024-07-15
  • 通讯作者: 吕子钰 E-mail:huangxyu@mail.ustc.edu.cn;luziyucrystal@163.com
  • 作者简介:黄兴宇(1998—),男,硕士,研究方向为图模型. E-mail:huangxyu@mail.ustc.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(62002352);广东省基础与应用基础研究基金资助项目(2023A1515012848)

Category-wise knowledge probers for representation learning of graph neural networks

Xingyu HUANG1,2(),Mingyu ZHAO1,3,Ziyu LYU1,2,*()   

  1. 1. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong, China
    2. School of Cyber Science and Technology, Sun Yat-sen University, Shenzhen 518107, Guangdong, China
    3. University of Chinese Academy of Sciences, Beijing 100101, China
  • Received:2023-11-24 Online:2024-07-20 Published:2024-07-15
  • Contact: Ziyu LYU E-mail:huangxyu@mail.ustc.edu.cn;luziyucrystal@163.com

摘要:

针对图神经网络(graph neural network, GNN)模型缺乏相应的探针这一问题,提出面向图神经网络表征学习的知识探测框架,基于不同领域数据的类别属性设计2种类别感知的知识探针,分别为聚类探针和对比聚类探针。2种探针分别探测不同模型的表征效果并给出相应的分数。在引用网络、社交网络和生物网络等3个邻域的8个数据集上,对7个经典的图神经网络模型的表征学习实现了系统性地知识探测和评估实验,归纳出探测和评估结论。

关键词: 图神经网络, 知识探针, 模型评价, 表征学习

Abstract:

In order to solve the problem that the graph neural network model lacks corresponding probes, a knowledge detection framework for graph neural network representation learning is proposed, and two kinds of class-aware knowledge probes are designed based on the category attributes of data in different domains, namely clustering probes and contrastive clustering probes. The two probe the characterization effect of different models and give corresponding scores. On 8 datasets in 3 neighborhoods, including reference networks, social networks and biological networks, the representation learning of 7 classical graph neural network models realizes systematic knowledge detection and evaluation experiments, and summarizes the detection and evaluation conclusions.

Key words: graph neural network, knowledge probing, model evaluation, representation learning

中图分类号: 

  • TP391

图1

类别探针的探查方法"

图2

特征向量中的类别信息与空间分布"

表1

受测的谱域和空域图卷积神经网络"

图嵌入模型图 神经网络 特点 下游任务
节点分类 边预测 图分类
谱域 Chebyshev 切比雪夫多项式近似拟合
SSGCN 马尔可夫扩散核
空域 GCN 拉普拉斯正则项
LightGCN 推荐任务中的简化GCN
GraphSAGE 随机定长采样策略
GAT 自注意力机制
GIN 可微分非线性函数

表2

图嵌入模型在不同下游任务中的结果(链接预测Recall@20)以及其他任务的准确性"

神经网络 节点分类直推式 节点分类归纳式 边预测 图分类
Cora Citeseer PPI Flickr Yelp MovieLens MUTAG ENZYMES
Chebyshev 20.1* 19.9 45.8 44.0 5.10* 5.75* 71.7 20.0
SSGCN 43.6 29.1 38.8 44.8 8.87 7.09 61.5 18.3
GCN 35.8 26.3 46.1 43.9 8.72 6.55 66.7 17.5
LightGCN 21.6 18.1 39.6 45.2 5.27 6.62 61.5 15.0*
GraphSAGE 39.3 21.7 38.7* 42.8* 7.27 7.78 74.4 15.8
GAT 47.8 35.0 43.3 45.4 7.18 6.20 61.4* 20.8
GIN 40.2 25.0 45.7 45.3 7.46 7.82 71.8 20.8
MLP 15.8 16.4* 39.6 45.3 5.14 7.05 71.8 17.5

表3

各模型聚类探针得分"

特征初始化 神经网络 直推式 归纳式
节点分类 边预测 节点分类 图分类
Cora Citeseer Pubmed Cora Citeseer Pubmed Flickr Mutag Proteins Enzymes
rand64 Chebyshev 12.009 20.165 72.238 31.191 42.054 91.277 39.503 0.973 1.840 2.690
SSGCN 5.486 14.794 32.226 10.837 19.565 22.696 55.523 4.210* 3.360 9.800
GCN 5.768 12.439 37.887 13.860 25.343 36.635 48.492 1.330 2.140 3.280
LightGCN 13.457 22.640 80.393 21.463 41.126 48.426 67.668* 4.190 4.010* 9.920*
GraphSAGE 4.861 16.684 31.617 40.150* 72.680* 226.829 20.293 1.030 1.710 3.180
GAT 4.130 9.288 13.888 13.624 18.929 39.145 29.749 1.890 2.590 3.390
GIN 4.685 8.543 11.721 8.953 17.695 15.999 25.363 2.380 3.600 6.370
MLP 25.708* 51.193* 130.612* 38.894 50.574 304.132* 59.705 1.130 2.130 4.650
meta Chebyshev 1.414 2.288 1.411 43.538* 48.376* 363.183* 10.549 0.955 1.760 2.650
SSGCN 1.156 2.126 1.486 4.705 5.079 40.599 15.900 1.020* 1.790 11.100
GCN 1.139 2.080 1.372 6.107 6.347 36.815 9.352 0.959 1.830* 4.270
LightGCN 1.404 2.202 1.559 11.948 13.196 5.316 11.857 1.020* 1.790 13.000*
GraphSAGE 1.213 2.672 1.328 40.289 44.554 56.649 10.009 1.020* 1.790 2.970
GAT 1.116 2.077 1.370 6.985 5.356 23.751 11.535 0.977 1.800 6.250
GIN 2.403 3.990 3.784* 5.945 6.143 2.281 20.049* 0.941 1.660 4.320
MLP 3.644* 4.808* 3.674 16.824 37.931 33.164 8.206 0.929 1.790 2.590

表4

各模型对比聚类探针得分"

特征初始化 神经网络 直推式 归纳式
节点分类 边预测 节点分类 图分类
Cora Citeseer Pubmed Cora Citeseer Pubmed Flickr Mutag Proteins Enzymes
rand64 Chebyshev 9.792 28.594 128.167* 31.398 60.836 135.442 23.610 0.736 0.873 1.558
SSGCN 3.309 12.486 13.542 6.364 10.814 8.344 32.690 1.174 1.962 5.921
GCN 4.696 16.306 100.546 8.339 29.539 19.237 74.333* 0.905 0.853 1.571
LightGCN 8.637 16.282 45.771 19.462 35.508 32.117 31.168 1.052 1.969 5.806
GraphSAGE 4.394 23.678 32.052 38.923 86.968 41.095 18.306 0.447 0.828 1.619
GAT 2.954 7.742 11.671 41.778* 107.861* 173.833* 23.108 0.743 0.976 2.373
GIN 2.927 5.268 4.172 8.644 15.958 10.836 20.066 2.137* 2.117* 8.019*
MLP 48.342* 79.993* 69.205 35.218 48.810 68.031 36.763 0.739 1.274 3.300
meta Chebyshev 1.463 2.018 1.309 20.779 54.636* 24.330 9.315 1.082* 1.943 1.981
SSGCN 1.189 2.181 1.293 2.193 2.528 19.252 9.310 1.009 1.839 7.900
GCN 1.328 1.940 1.273 4.800 6.845 21.449 17.373 0.921 1.819 3.130
LightGCN 1.506 1.953 1.241 6.965 12.028 5.045 11.320 0.867 1.929 10.163*
GraphSAGE 1.159 2.231 1.535 45.428* 45.748 44.089* 10.774 0.928 1.288 1.216
GAT 1.162 2.041 1.233 6.178 8.451 38.720 9.696 0.850 1.850 7.037
GIN 1.864 3.143 1.727 4.223 4.336 3.066 12.154 0.772 2.095* 2.630
MLP 2.602* 3.897* 1.846* 11.797 16.880 13.051 19.922* 0.859 1.799 1.889

图3

图嵌入模型的雷达图"

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