Metadata-Version: 2.1
Name: graphgallery
Version: 0.4.1
Summary: Geometric Deep Learning Extension Library for TensorFlow and PyTorch
Home-page: https://github.com/EdisonLeeeee/GraphGallery
Author: Jintang Li
Author-email: cnljt@outlook.com
License: MIT LICENSE
Download-URL: https://github.com/EdisonLeeeee/GraphGallery/archive/0.4.1.tar.gz
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        <p align="center"><strong><em>TensorFLow</em> or <em>PyTorch</em>? Both!</strong></p>
        <!-- <p align="center"><strong>A <em>gallery</em> of state-of-the-art Graph Neural Networks (GNNs) for TensorFlow and PyTorch</strong>.</p> -->
        
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        # GraphGallery
        GraphGallery is a gallery of state-of-the-arts graph neural networks for [TensorFlow 2.x](https://github.com/tensorflow/tensorflow) and [PyTorch](https://github.com/pytorch/pytorch). GraphGallery 0.4.x is a total re-write from previous versions, and some things have changed. 
        
        # What's important
        Difference between GraphGallery and [pytorch geometric (PyG)](https://github.com/rusty1s/pytorch_geometric), [deep graph library (DGL)](https://github.com/dmlc/dgl), etc...
        + PyG, DGL are just like **TensorFlow**, but GraphGallery is more like **Keras**
        + GraphGallery is more friendly to use
        + GraphGallery is more efficiient
        
        
        # Installation
        + Build from source (latest version)
        ```bash
        git clone https://github.com/EdisonLeeeee/GraphGallery.git
        cd GraphGallery
        python setup.py install
        ```
        + Or using pip (stable version)
        ```bash
        pip install -U graphgallery
        ```
        # Implementations
        In detail, the following methods are currently implemented:
        
        ## Semi-supervised models
        ### General models
        
        + **ChebyNet** from *Michaël Defferrard et al*, [📝Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://arxiv.org/abs/1606.09375), *NIPS'16*. 
         [[🌋 TF]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/TensorFlow/test_ChebyNet.ipynb)
        + **GCN** from *Thomas N. Kipf et al*, [📝Semi-Supervised Classification with Graph Convolutional Networks](https://arxiv.org/abs/1609.02907), *ICLR'17*. 
         [[🌋 TF]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/TensorFlow/test_GCN.ipynb), [[🔥 Torch]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyTorch/test_GCN.ipynb)
        + **GraphSAGE** from *William L. Hamilton et al*, [📝Inductive Representation Learning on Large Graphs](https://arxiv.org/abs/1706.02216), *NIPS'17*. 
         [[🌋 TF]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/TensorFlow/test_GraphSAGE.ipynb)
        + **FastGCN** from *Jie Chen et al*, [FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling](https://arxiv.org/abs/1801.10247), *ICLR'18*. 
         [[🌋 TF]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/TensorFlow/test_FastGCN.ipynb)
        + **LGCN** from  *Hongyang Gao et al*, [📝Large-Scale Learnable Graph Convolutional Networks](https://arxiv.org/abs/1808.03965), *KDD'18*. 
         [[🌋 TF]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/TensorFlow/test_LGCN.ipynb)
        + **GAT** from *Petar Veličković et al*, [📝Graph Attention Networks](https://arxiv.org/abs/1710.10903), *ICLR'18*. 
         ), [[🌋 TF]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/TensorFlow/test_GAT.ipynb), [[🔥 Torch]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyTorch/test_GAT.ipynb)
        + **SGC** from *Felix Wu et al*, [📝Simplifying Graph Convolutional Networks](https://arxiv.org/abs/1902.07153), *ICML'19*. 
         [[🌋 TF]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/TensorFlow/test_SGC.ipynb),  [[🔥 Torch]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyTorch/test_SGC.ipynb)
        + **GWNN** from *Bingbing Xu et al*, [📝Graph Wavelet Neural Network](https://arxiv.org/abs/1904.07785), *ICLR'19*. 
         [[🌋 TF]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/TensorFlow/test_GWNN.ipynb)
        + **GMNN** from *Meng Qu et al*, [📝Graph Markov Neural Networks](https://arxiv.org/abs/1905.06214), *ICML'19*. 
         [[🌋 TF]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/TensorFlow/test_GMNN.ipynb)
        + **ClusterGCN** from *Wei-Lin Chiang et al*, [📝Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks](https://arxiv.org/abs/1905.07953), *KDD'19*. 
        [[🌋 TF]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/TensorFlow/test_ClusterGCN.ipynb), [[🔥 Torch]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/PyTorch/test_ClusterGCN.ipynb)
        + **DAGNN** from *Meng Liu et al*, [📝Towards Deeper Graph Neural Networks](https://arxiv.org/abs/2007.09296), *KDD'20*. 
         [[🌋 TF]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/TensorFlow/test_DAGNN.ipynb)
        
        
        ### Defense models
        + **RobustGCN** from *Dingyuan Zhu et al*, [📝Robust Graph Convolutional Networks Against Adversarial Attacks](https://dl.acm.org/doi/10.1145/3292500.3330851), *KDD'19*. 
        [[🌋 TF]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/TensorFlow/test_RobustGCN.ipynb)
        + **SBVAT** from *Zhijie Deng et al*, [📝Batch Virtual Adversarial Training for Graph Convolutional Networks](https://arxiv.org/abs/1902.09192), *ICML'19*. 
        [[🌋 TF]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/TensorFlow/test_SBVAT.ipynb)
        + **OBVAT** from *Zhijie Deng et al*, [📝Batch Virtual Adversarial Training for Graph Convolutional Networks](https://arxiv.org/abs/1902.09192), *ICML'19*. 
        [[🌋 TF]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/TensorFlow/test_OBVAT.ipynb)
         
        ## Unsupervised models
        + **Deepwalk** from *Bryan Perozzi et al*, [📝DeepWalk: Online Learning of Social Representations](https://arxiv.org/abs/1403.6652), *KDD'14*. 
         [[🌋 TF]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/TensorFlow/test_Deepwalk.ipynb)
        + **Node2vec** from *Aditya Grover et al*, [📝node2vec: Scalable Feature Learning for Networks](https://arxiv.org/abs/1607.00653), *KDD'16*. 
        [[🌋 TF]](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples/TensorFlow/test_Node2vec.ipynb)
        
        # Quick Start
        ## Datasets
        more details please refer to [GraphData](https://github.com/EdisonLeeeee/GraphData).
        ### Planetoid
        fixed datasets
        ```python
        from graphgallery.data import Planetoid
        # set `verbose=False` to avoid additional outputs 
        data = Planetoid('cora', verbose=False)
        graph = data.graph
        idx_train, idx_val, idx_test = data.split()
        # idx_train:  training indices: 1D Numpy array
        # idx_val:  validation indices: 1D Numpy array
        # idx_test:  testing indices: 1D Numpy array
        >>> graph
        Graph(adj_matrix(2708, 2708), attr_matrix(2708, 2708), labels(2708,))
        ```
        currently the supported datasets are:
        ```python
        >>> data.supported_datasets
        ('citeseer', 'cora', 'pubmed')
        ```
        ### NPZDataset
        more scalable datasets (stored with `.npz`)
        ```python
        from graphgallery.data import NPZDataset;
        # set `verbose=False` to avoid additional outputs
        data = NPZDataset('cora', verbose=False, standardize=False)
        graph = data.graph
        idx_train, idx_val, idx_test = data.split(random_state=42)
        >>> graph
        Graph(adj_matrix(2708, 2708), attr_matrix(2708, 2708), labels(2708,))
        ```
        currently the supported datasets are:
        ```python
        >>> data.supported_datasets
        ('citeseer', 'citeseer_full', 'cora', 'cora_ml', 'cora_full', 
         'amazon_cs', 'amazon_photo', 'coauthor_cs', 'coauthor_phy', 
         'polblogs', 'pubmed', 'flickr', 'blogcatalog')
        ```
        
        ## Tensor
        + Strided (dense) Tensor 
        ```python
        >>> backend()
        TensorFlow 2.1.2 Backend
        
        >>> from graphgallery import transforms as T
        >>> arr = [1, 2, 3]
        >>> T.astensor(arr)
        <tf.Tensor: shape=(3,), dtype=int32, numpy=array([1, 2, 3], dtype=int32)>
        
        ```
        
        + Sparse Tensor
        
        ```python
        >>> import scipy.sparse as sp
        >>> sp_matrix = sp.eye(3)
        >>> T.astensor(sp_matrix)
        <tensorflow.python.framework.sparse_tensor.SparseTensor at 0x7f1bbc205dd8>
        ```
        
        + also works for PyTorch, just like
        
        ```python
        >>> from graphgallery import set_backend
        >>> set_backend('torch') # torch, pytorch or th
        PyTorch 1.6.0+cu101 Backend
        
        >>> T.astensor(arr)
        tensor([1, 2, 3])
        
        >>> T.astensor(sp_matrix)
        tensor(indices=tensor([[0, 1, 2],
                               [0, 1, 2]]),
               values=tensor([1., 1., 1.]),
               size=(3, 3), nnz=3, layout=torch.sparse_coo)
        ```
        
        + To Numpy or Scipy sparse matrix
        ```python
        >>> tensor = T.astensor(arr)
        >>> T.tensoras(tensor)
        array([1, 2, 3])
        
        >>> sp_tensor = T.astensor(sp_matrix)
        >>> T.tensoras(sp_tensor)
        <3x3 sparse matrix of type '<class 'numpy.float32'>'
            with 3 stored elements in Compressed Sparse Row format>
        ```
        
        + Or even convert one Tensor to another one
        ```python
        >>> tensor = T.astensor(arr, kind="T")
        >>> tensor
        <tf.Tensor: shape=(3,), dtype=int64, numpy=array([1, 2, 3])>
        >>> T.tensor2tensor(tensor)
        tensor([1, 2, 3])
        
        >>> sp_tensor = T.astensor(sp_matrix, kind="T") # set kind="T" to convert to tensorflow tensor
        >>> sp_tensor
        <tensorflow.python.framework.sparse_tensor.SparseTensor at 0x7efb6836a898>
        >>> T.tensor2tensor(sp_tensor)
        tensor(indices=tensor([[0, 1, 2],
                               [0, 1, 2]]),
               values=tensor([1., 1., 1.]),
               size=(3, 3), nnz=3, layout=torch.sparse_coo)
        
        ```
        
        ## Example of GCN model
        ```python
        from graphgallery.nn.models import GCN
        
        model = GCN(graph, attr_transform="normalize_attr", device="CPU", seed=123)
        # build your GCN model with default hyper-parameters
        model.build()
        # train your model. here idx_train and idx_val are numpy arrays
        # verbose takes 0, 1, 2, 3, 4
        his = model.train(idx_train, idx_val, verbose=1, epochs=100)
        # test your model
        # verbose takes 0, 1
        loss, accuracy = model.test(idx_test, verbose=1)
        print(f'Test loss {loss:.5}, Test accuracy {accuracy:.2%}')
        ```
        On `Cora` dataset:
        ```
        Training...
        100/100 [==============================] - 1s 14ms/step - loss: 1.0161 - acc: 0.9500 - val_loss: 1.4101 - val_acc: 0.7740 - time: 1.4180
        Testing...
        1/1 [==============================] - 0s 62ms/step - test_loss: 1.4123 - test_acc: 0.8120 - time: 0.0620
        Test loss 1.4123, Test accuracy 81.20%
        ```
        ## Customization
        + Build your model
        you can use the following statement to build your model
        ```python
        # one hidden layer with hidden units 32 and activation function RELU
        >>> model.build(hiddens=32, activations='relu')
        
        # two hidden layer with hidden units 32, 64 and all activation functions are RELU
        >>> model.build(hiddens=[32, 64], activations='relu')
        
        # two hidden layer with hidden units 32, 64 and activation functions RELU and ELU
        >>> model.build(hiddens=[32, 64], activations=['relu', 'elu'])
        
        ```
        + Train your model
        ```python
        # train with validation
        >>> his = model.train(idx_train, idx_val, verbose=1, epochs=100)
        # train without validation
        >>> his = model.train(idx_train, verbose=1, epochs=100)
        ```
        here `his` is a tensorflow `History` instance.
        
        + Test you model
        ```python
        >>> loss, accuracy = model.test(idx_test, verbose=1)
        Testing...
        1/1 [==============================] - 0s 62ms/step - test_loss: 1.4123 - test_acc: 0.8120 - time: 0.0620
        >>> print(f'Test loss {loss:.5}, Test accuracy {accuracy:.2%}')
        Test loss 1.4123, Test accuracy 81.20%
        ```
        
        ## Visualization
        NOTE: you must install [SciencePlots](https://github.com/garrettj403/SciencePlots) package for a better preview.
        
        ```python
        import matplotlib.pyplot as plt
        with plt.style.context(['science', 'no-latex']):
            fig, axes = plt.subplots(1, 2, figsize=(15, 5))
            axes[0].plot(his.history['acc'], label='Train accuracy', linewidth=3)
            axes[0].plot(his.history['val_acc'], label='Val accuracy', linewidth=3)
            axes[0].legend(fontsize=20)
            axes[0].set_title('Accuracy', fontsize=20)
            axes[0].set_xlabel('Epochs', fontsize=20)
            axes[0].set_ylabel('Accuracy', fontsize=20)
        
            axes[1].plot(his.history['loss'], label='Training loss', linewidth=3)
            axes[1].plot(his.history['val_loss'], label='Validation loss', linewidth=3)
            axes[1].legend(fontsize=20)
            axes[1].set_title('Loss', fontsize=20)
            axes[1].set_xlabel('Epochs', fontsize=20)
            axes[1].set_ylabel('Loss', fontsize=20)
            
            plt.autoscale(tight=True)
            plt.show()        
        ```
        ![visualization](https://github.com/EdisonLeeeee/GraphGallery/blob/master/imgs/history.png)
        
        ## Using TensorFlow/PyTorch Backend
        ```python
        >>> import graphgallery
        >>> graphgallery.backend()
        TensorFlow 2.1.0 Backend
        
        >>> graphgallery.set_backend("pytorch")
        PyTorch 1.6.0+cu101 Backend
        ```
        GCN using PyTorch backend
        ```python
        
        # The following codes are the same with TensorFlow Backend
        >>> from graphgallery.nn.models import GCN
        >>> model = GCN(graph, attr_transform="normalize_attr", device="GPU", seed=123);
        >>> model.build()
        >>> his = model.train(idx_train, idx_val, verbose=1, epochs=100)
        Training...
        100/100 [==============================] - 0s 5ms/step - loss: 0.6813 - acc: 0.9214 - val_loss: 1.0506 - val_acc: 0.7820 - time: 0.4734
        >>> loss, accuracy = model.test(idx_test, verbose=1)
        Testing...
        1/1 [==============================] - 0s 1ms/step - test_loss: 1.0131 - test_acc: 0.8220 - time: 0.0013
        >>> print(f'Test loss {loss:.5}, Test accuracy {accuracy:.2%}')
        Test loss 1.0131, Test accuracy 82.20%
        
        ```
        
        # How to add your custom datasets
        This is motivated by [gnn-benchmark](https://github.com/shchur/gnn-benchmark/)
        ```python
        from graphgallery.data import Graph
        
        # Load the adjacency matrix A, attribute matrix X and labels vector y
        # A - scipy.sparse.csr_matrix of shape [n_nodes, n_nodes]
        # X - scipy.sparse.csr_matrix or np.ndarray of shape [n_nodes, n_atts]
        # y - np.ndarray of shape [n_nodes]
        
        mydataset = Graph(adj_matrix=A, attr_matrix=X, labels=y)
        # save dataset
        mydataset.to_npz('path/to/mydataset.npz')
        # load dataset
        mydataset = Graph.from_npz('path/to/mydataset.npz')
        ```
        
        # How to define your custom models
        TODO
        
        # More Examples
        Please refer to the [examples](https://github.com/EdisonLeeeee/GraphGallery/blob/master/examples) directory.
        
        # TODO Lists
        - [x] Add PyTorch models support
        - [ ] Add more GNN models (TF and Torch backend)
        - [ ] Support for more tasks, e.g., `graph Classification` and `link prediction`
        - [ ] Support for more types of graphs, e.g., Heterogeneous graph
        - [ ] Add Docstrings and Documentation (Building)
        
        
        # Acknowledgement
        This project is motivated by [Pytorch Geometric](https://github.com/rusty1s/pytorch_geometric), [Tensorflow Geometric](https://github.com/CrawlScript/tf_geometric) and [Stellargraph](https://github.com/stellargraph/stellargraph), and the original implementations of the authors, thanks for their excellent works!
        
        
Keywords: tensorflow,pytorch,geometric-deep-learning,graph-neural-networks
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
