Link: https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.conv.SAGEConv.html
Description: WEBclass SAGEConv ( in_channels: Union[int, Tuple[int, int]], out_channels: int, aggr: Optional[Union[str, List[str], Aggregation]] = 'mean', normalize: bool = False, root_weight: bool = True, project: bool = False, bias: bool = True, **kwargs) [source] . Bases: MessagePassing.
DA: 45 PA: 50 MOZ Rank: 95
Link: https://docs.dgl.ai/en/1.1.x/generated/dgl.nn.pytorch.conv.SAGEConv.html
Description: WEBSAGEConv¶ class dgl.nn.pytorch.conv. SAGEConv (in_feats, out_feats, aggregator_type, feat_drop = 0.0, bias = True, norm = None, activation = None) [source] ¶ Bases: torch.nn.modules.module.Module. GraphSAGE layer from Inductive Representation Learning on Large Graphs
DA: 87 PA: 3 MOZ Rank: 37
Link: https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html
Description: WEBFinally, we added full support for customization of aggregations into the SAGEConv layer — simply override its aggr argument and utilize the power of aggregation within your GNN. Note You can read more about the torch_geometric.nn.aggr package in this blog post .
DA: 87 PA: 21 MOZ Rank: 69
Link: https://pytorch-geometric.readthedocs.io/en/latest/_modules/torch_geometric/nn/conv/sage_conv.html
Description: WEBclass SAGEConv (MessagePassing): r """The GraphSAGE operator from the `"Inductive Representation Learning on Large Graphs" <https://arxiv.org/abs/1706.02216>`_ paper... math:: \mathbf{x}^{\prime}_i = \mathbf{W}_1 \mathbf{x}_i + \mathbf{W}_2 \cdot \mathrm{mean}_{j \in \mathcal{N(i)}} \mathbf{x}_j If :obj:`project = True`, then …
DA: 29 PA: 5 MOZ Rank: 37
Link: https://medium.com/@sheikh.sahil12299/exploring-sageconv-a-powerful-graph-neural-network-architecture-44b7974b1fe0
Description: WEBMay 1, 2023 · SageConv is an improvement over GraphSAGE in that it uses a more expressive convolutional operator, which allows it to capture more complex features. The key difference between SageConv and...
DA: 30 PA: 42 MOZ Rank: 49
Link: https://towardsdatascience.com/pytorch-geometric-graph-embedding-da71d614c3a
Description: WEBSep 3, 2021 · Using SAGEConv in PyTorch Geometric module for embedding graphs. Graph representation learning/embedding is commonly the term used for the process where we transform a Graph data structure to a more structured vector form. This enables the downstream analysis by providing more manageable fixed-length vectors.
DA: 27 PA: 48 MOZ Rank: 72
Link: https://docs.dgl.ai/en/latest/generated/dgl.nn.tensorflow.conv.SAGEConv.html
Description: WEBclass dgl.nn.tensorflow.conv.SAGEConv(*args: Any, **kwargs: Any) Bases: © Copyright 2018, DGL Team. Revision 00cb0f9e. Built with Sphinx using a theme provided by Read …
DA: 29 PA: 26 MOZ Rank: 57
Link: https://www.youtube.com/watch?v=qA6U4nIK62E
Description: WEBMar 27, 2021 · 2.74K subscribers. Subscribed. 299. 21K views 3 years ago Pytroch Geometric Tutorials: In this tutorial, we present Graph Autoencoders and Variational Graph Autoencoders from the paper:...
DA: 14 PA: 49 MOZ Rank: 37
Link: https://stackoverflow.com/questions/78337332/alternative-to-sageconv-to-support-edge-weights-in-pytorch-geometric
Description: WEB3 days ago · I want to use graphSAGE to find abnormal nodes. I want to use edge weights for my graphs which is not supported in graphSAGE using (PYG) PyTorch Geometric library. If I use GraphConv instead of SageConv, which supports edge weight for the input graph, then is the function of GraphConv similar to SageConv? And is GraphConv …
DA: 95 PA: 97 MOZ Rank: 70
Link: https://docs.dgl.ai/en/0.8.x/generated/dgl.nn.tensorflow.conv.SAGEConv.html
Description: WEBSAGEConv. ChebConv. SGConv. APPNPConv. GINConv. Global Pooling Layers. Heterogeneous Learning Modules. dgl.nn (MXNet) dgl.nn.functional. dgl.ops. dgl.optim.
DA: 94 PA: 31 MOZ Rank: 99
