GraphGallery 0.1.11

update for 0.1.11.

GraphGallery 0.1.10

Changes

• rename do_forward to train_step and test_step
• Add datasets support, Planetoid and SimpleGraphDataset:

from graphgallery.data import SimpleGraphDataset, Planetoid

data =  Planetoid('cora') # cora, citeseer, pubmed
# or 
data = SimpleGraphDataset('cora') # cora, citeseer, pubmed, cora_ml, polblogs

which consists of

adj = data.adj # scipy sprase matrix
x = data.x  # numpy array
labels = data.labels  # numpy array
idx_train = data.idx_train  # numpy array
idx_val = data.idx_val  # numpy array
idx_test = data.idx_test  # numpy array

for more details, please refer to https://github.com/EdisonLeeeee/GraphData

GraphGallery 0.1.9

Changes

• Add datasets support, Planetoid and SimpleGraphDataset:

from graphgallery.data import SimpleGraphDataset, Planetoid

data =  Planetoid('cora') # cora, citeseer, pubmed
# or 
data = SimpleGraphDataset('cora') # cora, citeseer, pubmed, cora_ml, polblogs

which consists of

adj = data.adj # scipy sprase matrix
x = data.x  # numpy array
labels = data.labels  # numpy array
idx_train = data.idx_train  # numpy array
idx_val = data.idx_val  # numpy array
idx_test = data.idx_test  # numpy array

for more details, please refer to https://github.com/EdisonLeeeee/GraphData

GraphGallery 0.1.8

Changes:

• Add DAGNN implementation
• fix some typos
• rewrite __getattr__ in BaseModel, so most of the attributes can be used when calling model.attr instead of model.model.attr, e.g.,
  • model.summary equals model.model.summary if has this attribute
  • model.layers equals model.model.layers if has this attribute
  • model.weights equals model.model.weights if has this attribute
  • others like get_weights(), trainable_variables, variables are the same

GraphGallery 0.1.7

Changes

New features:

• reset_lr, reset_optimizer, reset_weights for semi-supervised models
• remove the last softmax activation function, so the output of the model will be a non-activated logit
• add null context manager
• add Gather layer

Bug fix

• astensor rename to astensors and add astensor method
• infer_type can accept scipy sparse matrix and tensorflow tensor
• LGCN can accept dense matrix
• fix normalize_adj bug when accept dense matrix

Others

• move GCNF, EdgeGCN, SimplifiedOBVAT, MedianSAGE, GCN_MIX to graphgallery/nn/models/semisupervised/experimental/
• by default, norm_x=None instead of norm_x='l1'
• set default integer type int32 instead of int64

Example of GCN model

from graphgallery.nn.models import GCN
# adj is scipy sparse matrix, x is numpy array matrix
model = GCN(adj, x, labels, device='GPU', norm_x='l1', seed=123)
# build your GCN model with custom hyper-parameters
model.build()
# train your model. here idx_train and idx_val are numpy arrays
his = model.train(idx_train, idx_val, verbose=1, epochs=100)
# test your model
loss, accuracy = model.test(idx_test)
print(f'Test loss {loss:.5}, Test accuracy {accuracy:.2%}')

On Cora dataset:

loss 1.02, acc 95.00%, val_loss 1.41, val_acc 77.40%: 100%|██████████| 100/100 [00:02<00:00, 37.07it/s]
Test loss 1.4123, Test accuracy 81.20%

Build your model

you can use the following statement to build your model

# 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'])

# other parameters like `dropouts` and `l2_norms` (if have) are the SAME.

Train or test your model

More details can be seen in the methods model.train and model.test

Hyper-parameters

you can simply use model.show() to show all your Hyper-parameters.
Otherwise you can also use model.show('model') or model.show('train') to show your model parameters and training parameters.
NOTE: you should install texttable first.

Visualization

• Accuracy

import matplotlib.pyplot as plt
plt.plot(his.history['acc'])
plt.plot(his.history['val_acc'])
plt.legend(['Accuracy', 'Val Accuracy'])
plt.xlabel('Epochs')

• Loss

import matplotlib.pyplot as plt
plt.plot(his.history['loss'])
plt.plot(his.history['val_loss'])
plt.legend(['Loss', 'Val Loss'])
plt.xlabel('Epochs')

GraphGallery 0.1.6

Changes

• rename SqueezedSparseConversion -> `SparseConversion``
• rename norm_adj_rate -> norm_adj, norm_x_type -> norm_x
• rename SupervisedModel -> SemiSupervisedModel
• add function is_scalar_like
• add __repr__ method for SemiSupervisedModel and UnsupervisedModel
• fix tqdm bug which sometimes show previous results.
• fix astensor cannot accept scalar bug
• fix some typos and bugs

Example of GCN model

from graphgallery.nn.models import GCN
# adj is scipy sparse matrix, x is numpy array matrix
model = GCN(adj, x, labels, device='GPU', seed=123)
# build your GCN model with custom hyper-parameters
model.build()
# train your model. here idx_train and idx_val are numpy arrays
his = model.train(idx_train, idx_val, verbose=1, epochs=100)
# test your model
loss, accuracy = model.test(idx_test)
print(f'Test loss {loss:.5}, Test accuracy {accuracy:.2%}')

On Cora dataset:

loss 1.02, acc 95.00%, val_loss 1.41, val_acc 77.40%: 100%|██████████| 100/100 [00:02<00:00, 37.07it/s]
Test loss 1.4123, Test accuracy 81.20%

Build your model

you can use the following statement to build your model

# 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'])

# other parameters like `dropouts` and `l2_norms` (if have) are the SAME.

Train or test your model

More details can be seen in the methods model.train and model.test

Hyper-parameters

you can simply use model.show() to show all your Hyper-parameters.
Otherwise you can also use model.show('model') or model.show('train') to show your model parameters and training parameters.
NOTE: you should install texttable first.

Visualization

• Accuracy

import matplotlib.pyplot as plt
plt.plot(his.history['acc'])
plt.plot(his.history['val_acc'])
plt.legend(['Accuracy', 'Val Accuracy'])
plt.xlabel('Epochs')

• Loss

import matplotlib.pyplot as plt
plt.plot(his.history['loss'])
plt.plot(his.history['val_loss'])
plt.legend(['Loss', 'Val Loss'])
plt.xlabel('Epochs')

GraphGallery 0.1.5

Changes

• add model.show() methods to show the parameters.
• improve the method model.build and using it comes more flexible.
• fix the ClusterGCN retracing in tf.function bugs in tensorflow version >= 2.2.0.

Example of GCN model

from graphgallery.nn.models import GCN
# adj is scipy sparse matrix, x is numpy array matrix
model = GCN(adj, x, labels, device='GPU', seed=123)
# build your GCN model with custom hyper-parameters
model.build()
# train your model. here idx_train and idx_val are numpy arrays
his = model.train(idx_train, idx_val, verbose=1, epochs=100)
# test your model
loss, accuracy = model.test(idx_test)
print(f'Test loss {loss:.5}, Test accuracy {accuracy:.2%}')

On Cora dataset:

loss 1.02, acc 95.00%, val_loss 1.41, val_acc 77.40%: 100%|██████████| 100/100 [00:02<00:00, 37.07it/s]
Test loss 1.4123, Test accuracy 81.20%

Build your model

you can use the following statement to build your model

# 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'])

# other parameters like `dropouts` and `l2_norms` (if have) are the SAME.

Train or test your model

More details can be seen in the methods model.train and model.test

Hyper-parameters

you can simply use model.show() to show all your Hyper-parameters.
Otherwise you can also use model.show('model') or model.show('train') to show your model parameters and training parameters.
NOTE: you should install texttable first.

Visualization

• Accuracy

import matplotlib.pyplot as plt
plt.plot(his.history['acc'])
plt.plot(his.history['val_acc'])
plt.legend(['Accuracy', 'Val Accuracy'])
plt.xlabel('Epochs')

• Loss

import matplotlib.pyplot as plt
plt.plot(his.history['loss'])
plt.plot(his.history['val_loss'])
plt.legend(['Loss', 'Val Loss'])
plt.xlabel('Epochs')

Graphgallery 0.1.4

Frist stable version of Graphgallery, the following models are implemented:
General models

• GCN from Semi-Supervised Classification with Graph Convolutional Networks 🌐Paper
• GAT from Graph Attention Networks 🌐Paper
• SGC from Simplifying Graph Convolutional Networks 🌐Paper
• GraphSAGE from Inductive Representation Learning on Large Graphs 🌐Paper
• GWNN from Graph Wavelet Neural Network 🌐Paper
• GMNN from Graph Markov Neural Networks 🌐Paper
• ChebyNet from Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering 🌐Paper
• ClusterGCN from Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks 🌐Paper
• FastGCN from FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling 🌐Paper
• LGCN from Large-Scale Learnable Graph Convolutional Networks 🌐Paper

Defense models

• RGCN from Robust Graph Convolutional Networks Against Adversarial Attacks 🌐Paper
• SBVAT/OBVAT from Batch Virtual Adversarial Training for Graph Convolutional Networks 🌐Paper

Other models

• GCN_MIX: Mixture of GCN and MLP
• GCNF: GCN + feature
• DenseGCN: Dense version of GCN
• EdgeGCN: GCN using message passing framework
• MedianSAGE: GraphSAGE using Median aggregation