Metadata-Version: 2.1
Name: ChebyGCN
Version: 0.0.2
Summary: Implements graph convolution keras layers based on Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst, Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering, Neural Information Processing Systems (NIPS), 2016.
Home-page: https://github.com/aclyde11/ChebyGCN
Author: Austin Clyde
Author-email: aclyde@uchicago.edu
License: UNKNOWN
Description: # ChebyGCN
        
        ```python
        pip install ChebyGCN
        ```
        
        Notice, for training and testing data, permutations of the data must be done in a certain way to align with 
        pooling of the graph lapacian. Further, every level of graph corsening is a pool of size two, thus if you want to 
        pool by 2 and then 4, you need log_2(2 * 4)= 3 levels. You will also need to index your Lapancians as seen below.
        
        ```python 
        from ChebyGCN import layers, coarsening
        A = scipy.sparse.csr.csr_matrix(A) #load adjanecy matrix 
        graphs, perm = coarsening.coarsen(A, levels=3, self_connections=True) #produce graph coarsenings 
        X_train = coarsening.perm_data(X_train, perm)
        X_test = coarsening.perm_data(X_test, perm)
        L = [coarsening.laplacian(A, normalized=True) for A in graphs]
        
        x_input = Input(shape=(X_train.shape[1],))
        x = Reshape((X_train.shape[1],1))(x_input)
        x = layers.GraphConvolution( 8, 2, 20, L[0])(x)
        x = layers.GraphConvolution( 8, 4, 10, L[2])(x)
        x = Flatten()(x)
        x = Dense(66, activation='softmax')(x)
        ```
        
        
        This code is 96% based on https://github.com/mdeff/cnn_graph Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst, Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering, Neural Information Processing Systems (NIPS), 2016.
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 2
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
