Heterogeneous Information Network Representation Learning Framework Based on Graph Attention Network
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摘要: 常用的异质信息网络有知识图谱和具有简单模式层的异质信息网络，它们的表示学习通常遵循不同的方法。该文总结了知识图谱和具有简单模式层的异质信息网络之间的异同，提出了一个通用的异质信息网络表示学习框架。该文提出的框架可以分为3个部分：基础向量模型，基于图注意力网络的传播模型以及任务模型。基础向量模型用于学习基础的网络向量；传播模型通过堆叠注意力层学习网络的高阶邻居特征；可更换的任务模型适用于不同的应用场景。与基准模型相比，该文所提框架在知识图谱的链接预测任务和异质信息网络的节点分类任务中都取得了相对不错的效果。Abstract: Commonly used heterogeneous information networks include knowledge graphs and heterogeneous information networks with simple schemas. Their representation learning follows usually different methods. The similarities and differences between knowledge graphs and heterogeneous information networks with simple schemas are summarized, and a general heterogeneous information network representation learning framework is proposed. The proposed framework can be divided into three parts: the basic vector model, the graph attention network based propagation model, and the task model. The basic vector model is used to learn basic network vector; The propagation model learns the high-order neighbor features of the network by stacking attention layers. The replaceable task module is suitable for different application scenarios. Compared with the benchmark model, the proposed framework achieves relatively good results in the link prediction task of the knowledge graph and the node classification task of the heterogeneous information network.
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表 1 简单模式层异质信息网络数据集的统计信息
数据集 节点 #节点 关系 #连边 #训练 #验证 #测试 DBLP Paper(P) 13769 AP
PC
PT30632
13769
867392400 400 1200 Author(A) 13941 Conference(C) 20 Term(T) 8623 IMDB Movie(M) 5473 MA
MD15814
54731800 300 900 Actor(A) 6725 Director(D) 2761 
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表 2 简单模式层异质信息网络的节点分类性能
数据集 指标 DeepWalk Esim Metapath2vec HAN HE-GAN-NC Variant1 Variant2 DBLP Macro-F1 83.15 92.47 91.63 92.52 94.31 92.26 94.16 Micro-F1 85.77 93.60 92.64 93.67 95.17 93.42 94.81 IMDB Macro-F1 48.34 33.89 45.13 52.29 53.58 50.35 53.11 Micro-F1 52.48 35.25 49.38 55.86 57.92 53.96 57.32
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表 3 知识图谱的链接预测任务性能
数据集 FB15k-237 WN18RR 指标 MRR Hits@1 Hits@3 Hits@10 MRR Hits@1 Hits@3 Hits@10 TransE 0.279 0.19 0.38 0.44 0.242 0.04 0.44 0.53 ConvE 0.315 0.24 0.35 0.49 0.461 0.42 0.47 0.53 ConvKB 0.285 0.19 0.32 0.47 0.263 0.06 0.45 0.55 SACN 0.352 0.26 0.39 0.54 0.463 0.43 0.48 0.54 relationPrediction 0.518 0.46 0.54 0.63 0.440 0.36 0.48 0.58 HE-GAN-LP 0.523 0.46 0.56 0.66 0.468 0.41 0.50 0.59 Variant3 0.520 0.45 0.55 0.64 0.447 0.37 0.48 0.58
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