dgl.readout

Graph Readout

sum_nodes(graph, feat[, weight, ntype])

Syntax sugar for dgl.readout_nodes(graph, feat, weight, ntype=ntype, op='sum').

sum_edges(graph, feat[, weight, etype])

Syntax sugar for dgl.readout_edges(graph, feat, weight, etype=etype, op='sum').

mean_nodes(graph, feat[, weight, ntype])

Syntax sugar for dgl.readout_nodes(graph, feat, weight, ntype=ntype, op='mean').

mean_edges(graph, feat[, weight, etype])

Syntax sugar for dgl.readout_edges(graph, feat, weight, etype=etype, op='mean').

max_nodes(graph, feat[, weight, ntype])

Syntax sugar for dgl.readout_nodes(graph, feat, weight, ntype=ntype, op='max').

max_edges(graph, feat[, weight, etype])

Syntax sugar for dgl.readout_edges(graph, feat, weight, etype=etype, op='max').

topk_nodes(graph, feat, k, *[, descending, …])

Return a graph-level representation by a graph-wise top-k on node features feat in graph by feature at index sortby.

topk_edges(graph, feat, k, *[, descending, …])

Return a graph-level representation by a graph-wise top-k on edge features feat in graph by feature at index sortby.

softmax_nodes(graph, feat, *[, ntype])

Perform graph-wise softmax on the node features.

softmax_edges(graph, feat, *[, etype])

Perform graph-wise softmax on the edge features.

broadcast_nodes(graph, graph_feat, *[, ntype])

Generate a node feature equal to the graph-level feature graph_feat.

broadcast_edges(graph, graph_feat, *[, etype])

Generate an edge feature equal to the graph-level feature graph_feat.