In PyG, the subgraph function allows for extracting a subgraph based on a subset of nodes or edges. However, during subgraph extraction, the correspondence between the extracted subgraph and the original node features is not explicitly preserved. In contrast, DGL's node_subgraph function retains th...
dgl.node_subgraph, dgl.edge_subgraph, dgl.in_subgraph 和dgl.out_subgraph都增加一个relabel_nodes参数,以允许图形压缩 dgl.node_subgraph,dgl.edge_subgraph,dgl.in_subgraphanddgl.out_subgraphall have arelabel_nodesargument to allow graph compaction (i.e. removing the nodes with no edges). (#2929)...
(1) input nodes 为 采样的subgraph中的所有的节点的集合,前面做了两层采样,并且是以 节点0~3为target node做的两层采样,这里底层是对每个节点的subgraph做了合并,组成了一个大的subgraph,这个subgraph的节点数量一共是10个; (2)需要注意的是,一般采样默认的方向是 source ——> target,因此对于无向图问题而言...
使用DGL 的dgl.khop_in_subgraph或dgl.dfs_nodes_generator生成连通子图。 代码语言:javascript 复制 importdglimporttorch defget_connected_components(graph):visited=torch.zeros(graph.num_nodes(),dtype=torch.bool)components=[]defbfs(node):queue=[node]component=[]whilequeue:current=queue.pop(0)ifnot vis...
使用DGL 的dgl.khop_in_subgraph或dgl.dfs_nodes_generator生成连通子图。 import dgl import torch def get_connected_components(graph): visited = torch.zeros(graph.num_nodes(), dtype=torch.bool) components = [] def bfs(node): queue = [node] ...
Note that we allow two node types to have the same name as long as one belongs to SRC while the other belongs to DST. To distinguish them, prepend the name with "SRC/" or "DST/" when specifying a node type. 1. 2. 3. 4. ...
在DGL中每条关系使用三元组来表示(source node type, edge type, destination node type) >>>importdgl>>>importtorchasth>>># Create a heterograph with 3 node types and 3 edges types.>>>graph_data = {...('drug','interacts','drug'): (th.tensor([0,1]), th.tensor([1,2])),...('...
each subgraph is stored as aDGLGraphobject. The original node/edge IDs before reshuffling are stored in the field of ‘orig_id’ in the node/edge data of the subgraphs. The node data dgl.NID and the edge data dgl.EID of the subgraphs store new node/edge IDs of the full graph after ...
Learning Robust Node Representations on Graphs,Xu Chen, Ya Zhang, Ivor Tsang, Yuangang Pan Adaptive Graph Diffusion Networks with Hop-wise Attention,Chuxiong Sun, Guoshi Wu The Photoswitch Dataset: A Molecular Machine Learning Benchmark for the Advancement of Synthetic Chemistry,Aditya R. Thawani, ...
# input nodes 为 采样的subgraph中的所有的节点的集合 for input_nodes, pos_g, neg_g, blocks in tqdm.tqdm(spec_dataloader): emb = extract_embed(all_node_embed, input_nodes) pos_score, neg_score = model(emb, pos_g, neg_g, blocks, etype) loss = loss_func(pos_score, neg_score) lo...