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SherylHYX / pytorch_geometric_signed_directed

PyTorch Geometric Signed Directed is a signed/directed graph neural network extension library for PyTorch Geometric. The paper is accepted by LoG 2023.
https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/
MIT License
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deep-learning directed-networks gnn graph-neural-netowrks machine-learning networks python pytorch pytorch-geometric signed-networks

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Documentation | Case Study | Data Set Descriptions | Installation | Data Structures | External Resources | Paper

PyTorch Geometric Signed Directed is a signed and directed extension library for PyTorch Geometric. It follows the package structure in PyTorch Geometric Temporal.

The library consists of various signed and directed geometric deep learning, embedding, and clustering methods from a variety of published research papers and selected preprints. We also provide detailed examples in the [examples](https://github.com/SherylHYX/pytorch_geometric_signed_directed/tree/main/examples) folder. -------------------------------------------------------------------------------- **Citing** If you find *PyTorch Geometric Signed Directed* useful in your research, please consider adding the following citation: ```bibtex @inproceedings{he2024pytorch, title={Pytorch Geometric Signed Directed: A software package on graph neural networks for signed and directed graphs}, author={He, Yixuan and Zhang, Xitong and Huang, Junjie and Rozemberczki, Benedek and Cucuringu, Mihai and Reinert, Gesine}, booktitle={Learning on Graphs Conference}, pages={12--1}, year={2024}, organization={PMLR} } ``` -------------------------------------------------------------------------------- **Methods Included** In detail, the following signed or directed graph neural networks, as well as related methods designed for signed or directed netwroks, were implemented. **Directed Unsigned Network Models and Layers** * **[MagNet_node_classification](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/model.html#torch_geometric_signed_directed.nn.directed.MagNet_node_classification.MagNet_node_classification)** from Zhang *et al.*: [MagNet: A Neural Network for Directed Graphs.](https://arxiv.org/pdf/2102.11391.pdf) (NeurIPS 2021) * **[DiGCL](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/model.html#torch_geometric_signed_directed.nn.directed.DiGCL.DiGCL)** from Tong *et al.*: [Directed Graph Contrastive Learning.](https://proceedings.neurips.cc/paper/2021/file/a3048e47310d6efaa4b1eaf55227bc92-Paper.pdf) (NeurIPS 2021) * **[DiGCN_Inception_Block_node_classification](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/model.html#torch_geometric_signed_directed.nn.directed.DiGCN_node_classification.DiGCN_node_classification)** from Tong *et al.*: [Digraph Inception Convolutional Networks.](https://papers.nips.cc/paper/2020/file/cffb6e2288a630c2a787a64ccc67097c-Paper.pdf) (NeurIPS 2020) * **[DIGRAC_node_clustering](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/model.html#torch_geometric_signed_directed.nn.directed.DIGRAC_node_clustering.DIGRAC_node_clustering)** from He *et al.*: [DIGRAC: Digraph Clustering Based on Flow Imbalance.](https://proceedings.mlr.press/v198/he22b.html) (LoG 2022)

Expand to see all methods implemented for directed networks... * **[DGCN_node_classification](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/model.html#torch_geometric_signed_directed.nn.directed.DGCN_node_classification.DGCN_node_classification)** from Tong *et al.*: [Directed Graph Convolutional Network.](https://arxiv.org/pdf/2004.13970.pdf) (ArXiv 2020) * **[DiGCN_node_classification](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/model.html#torch_geometric_signed_directed.nn.directed.DiGCN_node_classification.DiGCN_node_classification)** from Tong *et al.*: [Digraph Inception Convolutional Networks.](https://papers.nips.cc/paper/2020/file/cffb6e2288a630c2a787a64ccc67097c-Paper.pdf) (NeurIPS 2020) * **[MagNet_link_prediction](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/model.html#torch_geometric_signed_directed.nn.directed.MagNet_link_prediction.MagNet_link_prediction)** from Zhang *et al.*: [MagNet: A Neural Network for Directed Graphs.](https://arxiv.org/pdf/2102.11391.pdf) (NeurIPS 2021) * **[DiGCN_link_prediction](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/model.html#torch_geometric_signed_directed.nn.directed.DiGCN_link_prediction.DiGCN_link_prediction)** from Tong *et al.*: [Digraph Inception Convolutional Networks.](https://papers.nips.cc/paper/2020/file/cffb6e2288a630c2a787a64ccc67097c-Paper.pdf) (NeurIPS 2020) * **[DiGCN_Inception_Block_link_prediction](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/model.html#torch_geometric_signed_directed.nn.directed.DiGCN_Inception_Block_link_prediction.DiGCN_Inception_Block_link_prediction)** from Tong *et al.*: [Digraph Inception Convolutional Networks.](https://papers.nips.cc/paper/2020/file/cffb6e2288a630c2a787a64ccc67097c-Paper.pdf) (NeurIPS 2020) * **[DGCN_link_prediction](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/model.html#torch_geometric_signed_directed.nn.directed.DGCN_link_prediction.DGCN_link_prediction)** from Tong *et al.*: [Directed Graph Convolutional Network.](https://arxiv.org/pdf/2004.13970.pdf) (ArXiv 2020) * **[DiGCN_Inception_Block](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/model.html#torch_geometric_signed_directed.nn.directed.DiGCN_Inception_Block.DiGCN_InceptionBlock)** from Tong *et al.*: [Digraph Inception Convolutional Networks.](https://papers.nips.cc/paper/2020/file/cffb6e2288a630c2a787a64ccc67097c-Paper.pdf) (NeurIPS 2020) * **[DGCNConv](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/model.html#torch_geometric_signed_directed.nn.directed.DGCNConv.DGCNConv)** from Tong *et al.*: [Directed Graph Convolutional Network.](https://arxiv.org/pdf/2004.13970.pdf) (ArXiv 2020) * **[MagNetConv](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/model.html#torch_geometric_signed_directed.nn.directed.MagNetConv.MagNetConv)** from Zhang *et al.*: [MagNet: A Neural Network for Directed Graphs.](https://arxiv.org/pdf/2102.11391.pdf) (NeurIPS 2021) * **[DiGCNConv](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/model.html#torch_geometric_signed_directed.nn.directed.DiGCNConv.DiGCNConv)** from Tong *et al.*: [Digraph Inception Convolutional Networks.](https://papers.nips.cc/paper/2020/file/cffb6e2288a630c2a787a64ccc67097c-Paper.pdf) (NeurIPS 2020) * **[DIMPA](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/model.html#torch_geometric_signed_directed.nn.directed.DIMPA.DIMPA)** from He *et al.*: [DIGRAC: Digraph Clustering Based on Flow Imbalance.](https://proceedings.mlr.press/v198/he22b.html) (LoG 2022)
**Signed (Directed) Network Models and Layers** * **[SSSNET_node_clustering](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/model.html#torch_geometric_signed_directed.nn.signed.SSSNET_node_clustering.SSSNET_node_clustering)** from He *et al.*: [SSSNET: Semi-Supervised Signed Network Clustering](https://arxiv.org/pdf/2110.06623.pdf) (SDM 2022) * **[SDGNN](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/model.html#torch_geometric_signed_directed.nn.signed.SDGNN.SDGNN)** from Huang *et al.*: [SDGNN: Learning Node Representation for Signed Directed Networks](https://arxiv.org/pdf/2101.02390.pdf) (AAAI 2021) * **[SiGAT](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/model.html#torch_geometric_signed_directed.nn.signed.SiGAT.SiGAT)** from Huang *et al.*: [Signed Graph Attention Networks](https://arxiv.org/pdf/1906.10958.pdf) (ICANN 2019) * **[MSGNN_link_prediction](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/model.html#torch_geometric_signed_directed.nn.general.MSGNN.MSGNN_link_prediction)** from He *et al.*: [MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian.](https://proceedings.mlr.press/v198/he22c.html) (LoG 2022)
Expand to see all methods implemented for signed networks... * **[MSGNN_node_classification](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/model.html#torch_geometric_signed_directed.nn.general.MSGNN.MSGNN_node_classification)** from He *et al.*: [MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian.](https://proceedings.mlr.press/v198/he22c.html) (LoG 2022) * **[MSConv](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/model.html#torch_geometric_signed_directed.nn.general.MSConv.MSConv)** from He *et al.*: [MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian.](https://proceedings.mlr.press/v198/he22c.html) (LoG 2022) * **[SSSNET_link_prediction](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/model.html#torch_geometric_signed_directed.nn.signed.SSSNET_link_prediction.SSSNET_link_prediction)** adapted from He *et al.*: [SSSNET: Semi-Supervised Signed Network Clustering](https://arxiv.org/pdf/2110.06623.pdf) (SDM 2022) * **[SNEA](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/model.html#torch_geometric_signed_directed.nn.signed.SNEA.SNEA)** from Li *et al.*: [Learning Signed Network Embedding via Graph Attention](https://ojs.aaai.org/index.php/AAAI/article/view/5911) (AAAI 2020) * **[SGCN](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/model.html#torch_geometric_signed_directed.nn.signed.SGCN.SGCN)** from Derr *et al.*: [Signed Graph Convolutional Networks](https://arxiv.org/pdf/1808.06354.pdf) (ICDM 2018) * **[SNEAConv](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/model.html#torch_geometric_signed_directed.nn.signed.SNEAConv.SNEAConv)** from Li *et al.*: [Learning Signed Network Embedding via Graph Attention](https://ojs.aaai.org/index.php/AAAI/article/view/5911) (AAAI 2020) * **[SGCNConv](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/model.html#torch_geometric_signed_directed.nn.signed.SGCNConv.SGCNConv)** from Derr *et al.*: [Signed Graph Convolutional Network](https://arxiv.org/pdf/1808.06354.pdf) (ICDM 2018) * **[SIMPA](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/model.html#torch_geometric_signed_directed.nn.signed.SIMPA.SIMPA)** from He *et al.*: [SSSNET: Semi-Supervised Signed Network Clustering](https://arxiv.org/pdf/2110.06623.pdf) (SDM 2022)
**Network Generation Methods** * **[Signed Stochastic Block Model(SSBM)](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/data.html#torch_geometric_signed_directed.data.signed.SSBM.SSBM)** from He *et al.*: [SSSNET: Semi-Supervised Signed Network Clustering](https://arxiv.org/pdf/2110.06623.pdf) (SDM 2022) * **[Polarized Signed Stochastic Block Model(POL-SSBM)](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/data.html#torch_geometric_signed_directed.data.signed.polarized_SSBM.polarized_SSBM)** from He *et al.*: [SSSNET: Semi-Supervised Signed Network Clustering](https://arxiv.org/pdf/2110.06623.pdf) (SDM 2022) * **[Directed Stochastic Block Model(DSBM)](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/data.html#torch_geometric_signed_directed.data.directed.DSBM.DSBM)** from He *et al.*: [DIGRAC: Digraph Clustering Based on Flow Imbalance.](https://proceedings.mlr.press/v198/he22b.html) (LoG 2022) * **[Signed Directed Stochastic Block Model(SDSBM)](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/data.html#torch_geometric_signed_directed.data.general.SDSBM.SDSBM)** from He *et al.*: [MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian.](https://proceedings.mlr.press/v198/he22c.html) (LoG 2022) **Data Loaders and Classes** * **[load_signed_real_data](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/data.html#torch_geometric_signed_directed.data.signed.load_signed_real_data.load_signed_real_data)** to load signed (directed) real-world data sets. * **[load_directed_real_data](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/data.html#torch_geometric_signed_directed.data.directed.load_directed_real_data.load_directed_real_data)** to load directed unsigned real-world data sets. * **[SignedData](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/data.html#torch_geometric_signed_directed.data.signed.SignedData.SignedData)** Signed Data Class. * **[DirectedData](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/data.html#torch_geometric_signed_directed.data.directed.DirectedData.DirectedData)** Directed Data Class.
Expand to see all data loaders and related methods... * **[SSSNET_signed_real_data](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/data.html#torch_geometric_signed_directed.data.signed.SSSNET_real_data.SSSNET_real_data)** to load signed real-world data sets from the SSSNET paper. * **[SDGNN_signed_real_data](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/data.html#torch_geometric_signed_directed.data.signed.SDGNN_real_data.SDGNN_real_data)** to load signed real-world data sets from the SDGNN paper. * **[MSGNN_signed_directed_real_data](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/data.html#torch_geometric_signed_directed.data.signed.MSGNN_real_data.MSGNN_real_data)** to load signed directed real-world data sets from the MSGNN paper. * **[DIGRAC_directed_real_data](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/data.html#torch_geometric_signed_directed.data.directed.DIGRAC_real_data.DIGRAC_real_data)** to load directed real-world data sets from the DIGRAC paper. * **[Telegram](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/data.html#torch_geometric_signed_directed.data.directed.Telegram.Telegram)** to load the Telegram data set. * **[Cora_ml](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/data.html#torch_geometric_signed_directed.data.directed.citation.Cora_ml)** to load the Cora_ML data set. * **[Citeseer](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/data.html#torch_geometric_signed_directed.data.directed.citation.Citeseer)** to load the CiteSeer data set. * **[WikiCS](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/data.html#torch_geometric_signed_directed.data.directed.WikiCS.WikiCS)** to load the WikiCS data set. * **[WikipediaNetwork](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/data.html#torch_geometric_signed_directed.data.directed.WikipediaNetwork.WikipediaNetwork)** to load the WikipediaNetwork data set.
**Task-Specific Objectives and Evaluation Methods** * **[Probabilistic Balanced Normalized Loss](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/utils.html#torch_geometric_signed_directed.utils.signed.prob_balanced_normalized_loss.Prob_Balanced_Normalized_Loss)** from He *et al.*: [SSSNET: Semi-Supervised Signed Network Clustering](https://arxiv.org/pdf/2110.06623.pdf) (SDM 2022) * **[Probabilistic Imbalance Objective](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/utils.html#torch_geometric_signed_directed.utils.directed.prob_imbalance_loss.Prob_Imbalance_Loss)** from He *et al.*: [DIGRAC: Digraph Clustering Based on Flow Imbalance.](https://proceedings.mlr.press/v198/he22b.html) (LoG 2022)
Expand to see all task-specific objectives and evaluation methods... * **[Probabilistic Balanced Ratio Loss](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/utils.html#torch_geometric_signed_directed.utils.signed.prob_balanced_ratio_loss.Prob_Balanced_Ratio_Loss)** from He *et al.*: [SSSNET: Semi-Supervised Signed Network Clustering](https://arxiv.org/pdf/2110.06623.pdf) (SDM 2022) * **[Unhappy Ratio](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/utils.html#torch_geometric_signed_directed.utils.signed.unhappy_ratio.Unhappy_Ratio)** from He *et al.*: [SSSNET: Semi-Supervised Signed Network Clustering](https://arxiv.org/pdf/2110.06623.pdf) (SDM 2022) * **[link_sign_prediction_logistic_function](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/utils.html#torch_geometric_signed_directed.utils.signed.link_sign_prediction_logistic_function.link_sign_prediction_logistic_function)** for signed networks' link sign prediction task. * **[link_sign_direction_prediction_logistic_function](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/utils.html#torch_geometric_signed_directed.utils.general.link_sign_direction_prediction_logistic_function.link_sign_prediction_logistic_function)** for signed directed networks' link prediction task. * **[triplet_loss_node_classification](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/utils.html#torch_geometric_signed_directed.utils.general.triplet_loss.triplet_loss_node_classification)** for triplet loss in the node classification task. * **[Sign_Triangle_Loss](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/model.html#torch_geometric_signed_directed.utils.signed.link_sign_loss.Sign_Triangle_Loss)** from Huang *et al.*: [SDGNN: Learning Node Representation for Signed Directed Networks](https://arxiv.org/pdf/2101.02390.pdf) (AAAI 2021) * **[Sign_Direction_Loss](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/model.html#torch_geometric_signed_directed.utils.signed.link_sign_loss.Sign_Direction_Loss)** from Huang *et al.*: [SDGNN: Learning Node Representation for Signed Directed Networks](https://arxiv.org/pdf/2101.02390.pdf) (AAAI 2021) * **[Sign_Product_Entropy_Loss](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/model.html#torch_geometric_signed_directed.utils.signed.link_sign_loss.Sign_Product_Entropy_Loss)** from Huang *et al.*: [SDGNN: Learning Node Representation for Signed Directed Networks](https://arxiv.org/pdf/2101.02390.pdf) (AAAI 2021) * **[Link_Sign_Product_Loss](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/model.html#torch_geometric_signed_directed.utils.signed.link_sign_loss.Link_Sign_Product_Loss)** from Huang *et al.*: [Signed Graph Attention Networks](https://arxiv.org/pdf/1906.10958.pdf) (ICANN 2019) * **[Link_Sign_Entropy_Loss](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/model.html#torch_geometric_signed_directed.utils.signed.link_sign_loss.Link_Sign_Entropy_Loss)** from Derr *et al.*: [Signed Graph Convolutional Network](https://arxiv.org/pdf/1808.06354.pdf) (ICDM 2018) * **[Sign_Structure_Loss](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/model.html#torch_geometric_signed_directed.utils.signed.link_sign_loss.Sign_Structure_Loss)**
**Utilities and Preprocessing Methods** * **[node_class_split](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/utils.html#torch_geometric_signed_directed.utils.general.node_split.node_class_split)** to split nodes into training set etc.. * **[link_class_split](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/utils.html#torch_geometric_signed_directed.utils.general.link_split.link_class_split)** to split edges into training set etc.. * **[get_magnetic_Laplacian](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/utils.html#torch_geometric_signed_directed.utils.directed.get_magnetic_Laplacian.get_magnetic_Laplacian)** from from Zhang *et al.*: [MagNet: A Neural Network for Directed Graphs.](https://arxiv.org/pdf/2102.11391.pdf) (NeurIPS 2021) * **[get_magnetic_signed_Laplacian](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/utils.html#torch_geometric_signed_directed.utils.general.get_magnetic_signed_Laplacian.get_magnetic_signed_Laplacian)** from He *et al.*: [MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian.](https://proceedings.mlr.press/v198/he22c.html) (LoG 2022)
Expand to see all utilities and preprocessing methods... * **[get_appr_directed_adj](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/utils.html#torch_geometric_signed_directed.utils.directed.get_adjs_DiGCN.get_appr_directed_adj)** from Tong *et al.*: [Digraph Inception Convolutional Networks.](https://papers.nips.cc/paper/2020/file/cffb6e2288a630c2a787a64ccc67097c-Paper.pdf) (NeurIPS 2020) * **[meta_graph_generation](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/utils.html#torch_geometric_signed_directed.utils.directed.meta_graph_generation.meta_graph_generation)** from He *et al.*: [DIGRAC: Digraph Clustering Based on Flow Imbalance.](https://proceedings.mlr.press/v198/he22b.html) (ArXiv 2021) * **[extract_network](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/utils.html#torch_geometric_signed_directed.utils.general.extract_network.extract_network)** from He *et al.*: [DIGRAC: Digraph Clustering Based on Flow Imbalance.](https://proceedings.mlr.press/v198/he22b.html) (LoG 2022) * **[directed_features_in_out](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/utils.html#torch_geometric_signed_directed.utils.directed.features_in_out.directed_features_in_out)** from Tong *et al.*: [Directed Graph Convolutional Network.](https://arxiv.org/pdf/2004.13970.pdf) (ArXiv 2020) * **[get_second_directed_adj](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/utils.html#torch_geometric_signed_directed.utils.directed.get_adjs_DiGCN.get_second_directed_adj)** from Tong *et al.*: [Digraph Inception Convolutional Networks.](https://papers.nips.cc/paper/2020/file/cffb6e2288a630c2a787a64ccc67097c-Paper.pdf) (NeurIPS 2020) * **[cal_fast_appr](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/utils.html#torch_geometric_signed_directed.utils.directed.get_adjs_DiGCN.cal_fast_appr)** from Tong *et al.*: [Digraph Inception Convolutional Networks.](https://papers.nips.cc/paper/2020/file/cffb6e2288a630c2a787a64ccc67097c-Paper.pdf) (NeurIPS 2020) * **[scipy_sparse_to_torch_sparse](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/utils.html#torch_geometric_signed_directed.utils.general.scipy_sparse_to_torch_sparse.scipy_sparse_to_torch_sparse)** from He *et al.*: [DIGRAC: Digraph Clustering Based on Flow Imbalance.](https://proceedings.mlr.press/v198/he22b.html) (LoG 2022) * **[create spectral features](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/modules/utils.html#torch_geometric_signed_directed.utils.signed.create_spectral_features.create_spectral_features)**
-------------------------------------------------------------------------------- Head over to our [documentation](https://pytorch-geometric-signed-directed.readthedocs.io/en/latest/) to find out more! If you notice anything unexpected, please open an [issue](https://github.com/SherylHYX/pytorch_geometric_signed_directed/issues). If you are missing a specific method, feel free to open a [feature request](https://github.com/SherylHYX/pytorch_geometric_signed_directed/issues). -------------------------------------------------------------------------------- **Installation** Binaries are provided for Python version >= 3.7 and NetworkX version < 2.7. After installing [PyTorch](https://pytorch.org/get-started/locally/) and [PyG](https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html), simply run ```sh pip install torch-geometric-signed-directed ``` -------------------------------------------------------------------------------- **Running tests** ``` $ python setup.py test ``` -------------------------------------------------------------------------------- **License** - [MIT License](https://github.com/SherylHYX/pytorch_geometric_signed_directed/blob/master/LICENSE)