CNNs on graphs with fast localized spectral filtering code revision The code can be used in classification the whole graph
https://blog.csdn.net/weixin_43279911/article/details/102920457 by Chinese
Change： 1.All functions are integrated into on one class,named neu_gcn 2.The length of node signal is changed to be greater than one
1.X：the data of one subject,size：(871, 116, 116) 871:the numbers of subjects,including training sets and testing sets 116x116:the feature matrix of one subject,each subject has one feature matrix(functional brain networks，.mat file)
2.Y: the labels of all subjects,size:(871,) 871: the numbers of all subjects
3.L:the adjacency matrix of nodes,size(116, 116) 116: the numbers of nodes, each subject has 116 nodes,each node has a feature vector(the length of the vector is 116)
Classificating the graph of datasets(one subject one graph),and get the roc/acc.
The model is not pre-training,so you can only use the model in your data. 1.Change the utils.py for load your datasets 2.Change the main.py for setting the X,Y,L 3.python main.py and get the results
1.TensorFlow 1.12.0 2.Python3.x