From b3f0eb3dbd6ead02e03216bd15309458286d6517 Mon Sep 17 00:00:00 2001 From: Akihiro Nitta Date: Fri, 9 Feb 2024 01:23:20 +0900 Subject: [PATCH] Replace arxiv pdf links with abs links (#8887) --- torch_geometric/datasets/rcdd.py | 2 +- torch_geometric/datasets/wikidata.py | 2 +- torch_geometric/nn/conv/han_conv.py | 2 +- torch_geometric/nn/conv/pdn_conv.py | 2 +- torch_geometric/nn/encoding.py | 2 +- torch_geometric/nn/models/visnet.py | 14 +++++++------- torch_geometric/nn/norm/mean_subtraction_norm.py | 2 +- 7 files changed, 13 insertions(+), 13 deletions(-) diff --git a/torch_geometric/datasets/rcdd.py b/torch_geometric/datasets/rcdd.py index 0af0c984601d..c83713229dd6 100644 --- a/torch_geometric/datasets/rcdd.py +++ b/torch_geometric/datasets/rcdd.py @@ -16,7 +16,7 @@ class RCDD(InMemoryDataset): r"""The risk commodity detection dataset (RCDD) from the `"Datasets and Interfaces for Benchmarking Heterogeneous Graph - Neural Networks" `_ paper. + Neural Networks" `_ paper. RCDD is an industrial-scale heterogeneous graph dataset based on a real risk detection scenario from Alibaba's e-commerce platform. It consists of 13,806,619 nodes and 157,814,864 edges across 7 node types diff --git a/torch_geometric/datasets/wikidata.py b/torch_geometric/datasets/wikidata.py index d4c97d87b8ea..75d025267dae 100644 --- a/torch_geometric/datasets/wikidata.py +++ b/torch_geometric/datasets/wikidata.py @@ -15,7 +15,7 @@ class Wikidata5M(InMemoryDataset): r"""The Wikidata-5M dataset from the `"KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation" - `_ paper, + `_ paper, containing 4,594,485 entities, 822 relations, 20,614,279 train triples, 5,163 validation triples, and 5,133 test triples. diff --git a/torch_geometric/nn/conv/han_conv.py b/torch_geometric/nn/conv/han_conv.py index 57e1edce3b8a..5cd8741be555 100644 --- a/torch_geometric/nn/conv/han_conv.py +++ b/torch_geometric/nn/conv/han_conv.py @@ -34,7 +34,7 @@ def group( class HANConv(MessagePassing): r"""The Heterogenous Graph Attention Operator from the `"Heterogenous Graph Attention Network" - `_ paper. + `_ paper. .. note:: diff --git a/torch_geometric/nn/conv/pdn_conv.py b/torch_geometric/nn/conv/pdn_conv.py index ddfd88955f97..1ca456d52127 100644 --- a/torch_geometric/nn/conv/pdn_conv.py +++ b/torch_geometric/nn/conv/pdn_conv.py @@ -12,7 +12,7 @@ class PDNConv(MessagePassing): r"""The pathfinder discovery network convolutional operator from the `"Pathfinder Discovery Networks for Neural Message Passing" - `_ paper. + `_ paper. .. math:: \mathbf{x}^{\prime}_i = \sum_{j \in \mathcal{N}(i) \cup diff --git a/torch_geometric/nn/encoding.py b/torch_geometric/nn/encoding.py index 6d67121b6e0c..aace00b11dae 100644 --- a/torch_geometric/nn/encoding.py +++ b/torch_geometric/nn/encoding.py @@ -6,7 +6,7 @@ class PositionalEncoding(torch.nn.Module): r"""The positional encoding scheme from the `"Attention Is All You Need" - `_ paper. + `_ paper. .. math:: diff --git a/torch_geometric/nn/models/visnet.py b/torch_geometric/nn/models/visnet.py index cdb8efd41ed6..c73ea9778cf3 100644 --- a/torch_geometric/nn/models/visnet.py +++ b/torch_geometric/nn/models/visnet.py @@ -340,7 +340,7 @@ def forward( class NeighborEmbedding(MessagePassing): r"""The :class:`NeighborEmbedding` module from the `"Enhancing Geometric Representations for Molecules with Equivariant Vector-Scalar Interactive - Message Passing" `_ paper. + Message Passing" `_ paper. Args: hidden_channels (int): The number of hidden channels in the node @@ -415,7 +415,7 @@ def message(self, x_j: Tensor, W: Tensor) -> Tensor: class EdgeEmbedding(torch.nn.Module): r"""The :class:`EdgeEmbedding` module from the `"Enhancing Geometric Representations for Molecules with Equivariant Vector-Scalar Interactive - Message Passing" `_ paper. + Message Passing" `_ paper. Args: num_rbf (int): The number of radial basis functions. @@ -458,7 +458,7 @@ class ViS_MP(MessagePassing): equivariant vector-scalar interactive graph neural network (ViSNet) from the `"Enhancing Geometric Representations for Molecules with Equivariant Vector-Scalar Interactive Message Passing" - `_ paper. + `_ paper. Args: num_heads (int): The number of attention heads. @@ -659,7 +659,7 @@ class ViS_MP_Vertex(ViS_MP): equivariant vector-scalar interactive graph neural network (ViSNet) from the `"Enhancing Geometric Representations for Molecules with Equivariant Vector-Scalar Interactive Message Passing" - `_ paper. + `_ paper. Args: num_heads (int): The number of attention heads. @@ -723,7 +723,7 @@ class ViSNetBlock(torch.nn.Module): r"""The representation module of the equivariant vector-scalar interactive graph neural network (ViSNet) from the `"Enhancing Geometric Representations for Molecules with Equivariant Vector-Scalar Interactive - Message Passing" `_ paper. + Message Passing" `_ paper. Args: lmax (int, optional): The maximum degree of the spherical harmonics. @@ -875,7 +875,7 @@ class GatedEquivariantBlock(torch.nn.Module): r"""Applies a gated equivariant operation to scalar features and vector features from the `"Enhancing Geometric Representations for Molecules with Equivariant Vector-Scalar Interactive Message Passing" - `_ paper. + `_ paper. Args: hidden_channels (int): The number of hidden channels in the node @@ -1039,7 +1039,7 @@ class ViSNet(torch.nn.Module): vector-scalar interactive graph neural network (ViSNet) from the `"Enhancing Geometric Representations for Molecules with Equivariant Vector-Scalar Interactive Message Passing" - `_ paper. + `_ paper. Args: lmax (int, optional): The maximum degree of the spherical harmonics. diff --git a/torch_geometric/nn/norm/mean_subtraction_norm.py b/torch_geometric/nn/norm/mean_subtraction_norm.py index 460ae13875e8..43df9dec5263 100644 --- a/torch_geometric/nn/norm/mean_subtraction_norm.py +++ b/torch_geometric/nn/norm/mean_subtraction_norm.py @@ -9,7 +9,7 @@ class MeanSubtractionNorm(torch.nn.Module): r"""Applies layer normalization by subtracting the mean from the inputs as described in the `"Revisiting 'Over-smoothing' in Deep GCNs" - `_ paper. + `_ paper. .. math:: \mathbf{x}_i = \mathbf{x}_i - \frac{1}{|\mathcal{V}|}