Python实现的图神经网络进行链接预测（link prediction） |
您所在的位置：网站首页 › gcn实例 › Python实现的图神经网络进行链接预测（link prediction） |
Python实现的图神经网络进行链接预测（link prediction）
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Guide IntroModelDatasetInstallCiteReference Intro本项目是对此前项目gcn_for_prediction_of_protein_interactions 的改动，使其应用于链接预测（link prediction），可以应用于两种数据集：a.带节点特征；b.不带节点特征。 a.带节点特征数据集如【data/cora】 b.不带节点特征数据集如【data/yeast】 Model 模型模型主要使用图神经网络，如gae、vgae等 1.GCNModelVAE(src/vgae)：图卷积自编码和变分图卷积自编码(config中可配置使用自编码或变分自编码)，利用gae/vgae作为编码器，InnerProductDecoder作解码器。 Variational Graph Auto-Encoders 。
2.GCNModelARGA(src/arga)：对抗正则化图自编码，利用gae/vgae作为生成器；一个三层前馈网络作判别器。 Adversarially Regularized Graph Autoencoder for Graph Embedding 。
3.GATModelVAE(src/graph_att_gae)：基于图注意力的图卷积自编码和变分图卷积自编码(config中可配置使用自编码或变分自编码)，利用gae/vgae作为编码器，InnerProductDecoder作解码器。这是我在以上【1】方法的基础上加入了一层图注意力层，关于图注意力可见【Reference】中的【GRAPH ATTENTION NETWORKS】。 4.GATModelGAN(src/graph_att_gan)：基于图注意力的对抗正则化图自编码，利用gae/vgae作为生成器；一个三层前馈网络作判别器，这是我在以上【2】方法的基础上加入了一层图注意力层，关于图注意力可见【Reference】中的【GRAPH ATTENTION NETWORKS】。 5.NHGATModelVAE(src/graph_nheads_att_gae)：基于图多头注意力的图卷积自编码和变分图卷积自编码(config中可配置使用自编码或变分自编码)，利用gae/vgae作为编码器，InnerProductDecoder作解码器。此方法是在【3】方法的基础上将图注意力层改为多头注意力层。 6.NHGATModelGAN(src/graph_nheads_att_gan)：基于图多头注意力的对抗正则化图自编码，利用gae/vgae作为生成器；一个三层前馈网络作判别器，此方法在【4】方法的基础上将图注意力层改为多头注意力层。 Note:程序【src/util/cora_class_one_hot_process.py】将cora数据集中cora.content中的节点label进行one-hot编码（处理后的数据集为：data/cora/cora.content_class），将其与features一起作为训练特征，这就相当于实现了条件变分自编码【conditional variational auroencoder (CVAE)】，从而在encoder与decoder中加入了节点label的限制。 Usage相关参数的配置config见每个模型文件夹中的config.cfg文件，训练和预测时会加载此文件。 训练及预测 1.model comparison(1).不带节点特征数据集【data/yeast】 GCNModelVAEGCNModelARGAGATModelVAEGATModelGANNHGATModelVAENHGATModelVAEepochs202020202020precision0.87080.87000.86290.87150.86720.8680roc_score0.88140.87980.87580.88260.87700.8767(2).带节点特征数据集【data/cora】 GCNModelVAEGCNModelARGAGATModelVAEGATModelGANNHGATModelVAENHGATModelVAEepochs200200200200200200precision0.74510.85440.85090.83920.80780.8156roc_score0.74040.84370.86860.83120.82780.82402.usage(1).train 是否带节点特征可通过每个model文件夹中的【config.cfg】进行配置，如下： config中有以下三个参数 node_cites_path = cora.cites # 节点间的引用数据 node_features_path = cora.content ＃ 节点特征数据 with_feats = True # 是否带节点特征。True, node_features_path不为None；False, node_features_path为None # train代码通用格式： from src.graph_nheads_att_gan.train import Train train = Train() train.train_model('config.cfg')(2).predict # predict代码通用格式: from src.graph_nheads_att_gan.predict import Predict predict = Predict() predict.load_model_adj('config_cfg') # 会返回原始的图邻接矩阵和经过模型编码后的hidden embedding经过内积解码的邻接矩阵，可以对这两个矩阵进行比对，得出link prediction. adj_orig, adj_rec = predict.predict() Dataset a. yeast dataset，【data/yeast】数据来自酵母蛋白质相互作用yeast 。 数据集的格式如下，具体可见data。 YLR418C YOL145C YOL145C YLR418C YLR418C YOR123C YOR123C YLR418C ...... ...... b. cora dataset，【data/cora】数据来自Machine Learning Paper的数据集cora 。 数据集的格式如下，具体可见data 。 cora.cites: 35 1033 35 103482 35 103515 35 1050679 ...... ......cora.content: 31336 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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