parameters(), lr=0.01, weight_decay=5e-4) model.train() for epoch in range(200): optimizer.zero_grad() out = model(data) loss = F.nll_loss(out[data.train_mask], data.y[data.train_mask]) loss.backward() optimizer.step() model.eval() _, pred = model(data).max(dim=1) ...
论文:Interest-aware Message-Passing GCN for Recommendation 地址:https://arxiv.org/abs/2102.10044 代码:https://github.com/liufancs/IMP_GCN 来自WWW2021的文章,探讨推荐系统中的过平滑问题。从何向南大佬的NGCF开始一直强调的就是高阶邻居的协作信号是可以学习良好的用户和项目嵌入。虽然GCN容易「过平滑」(即叠...
In the simplest case, the graph weights, denoted by the n×n symmetric matrix Gl for the lth layer, are binary, that is, Gl(i,j)=1 if X(:,i) and X(:,j) share the same label w.r.t. the features extracted at layer l. A smoothness regularization term is added to the loss ...
(default: False) --h_loss_weight Adaptive loss for neighbor reconstruction (default: 1.0) --feature_loss_weight Adaptive loss for feature reconstruction (default: 2.0) --degree_loss_weight Adaptive loss for degree reconstruction (default: 1.0) --calculate_contextual Flag for calculating Contextual ...
backward() # after loss.backward() pruner.regularize(model) # <== for sparse training optimizer.step() # before optimizer.step()Interactive PruningAll high-level pruners offer support for interactive pruning. You can utilize the method pruner.step(interactive=True) to retrieve all the groups ...
Spectral domain就是GCN的理论基础了。这种思路就是希望借助图谱的理论来实现拓扑图上的卷积操作。从整个...
“Methods”). Simultaneously, we calculated the SNV and CNV frequencies for each gene and calculated epigenetic densities within each gene promoter (see “Methods”). Then, we constructed a multi-omics information combination graph, in which the nodes represent genes and the edges are obtained ...
Deeper insights into graph convolutional networks for semi-supervised learning Proceedings of the AAAI Conference on Artificial Intelligence (2018) A.Tarvainenet al. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results ...
比如MotifNet: a motif-based Graph Convolutional Network for directed graphs 提出利用Graph Motifs定义图的邻接关系。 (b)如果只是为了应用,有其他形式的GCN或者GAT可以处理有向图 简而言之,要想简单地处理有向图问题,那就换成一种逐顶点(node-wise)的运算方式,可以参考下面这篇文章中的3.2及3.3节。 值得说明...
for epoch in range(training_epoch): for m in range(totalbatch): mini_batch = trainX[m * batch_size : (m+1) * batch_size] mini_label = trainY[m * batch_size : (m+1) * batch_size] _, loss1, rmse1, train_output = sess.run([optimizer, loss, error, y_pred], feed_dict ...