文件列表:

LICENSE (11357, 2021-05-22)
data (0, 2021-05-22)
data\PROTEINS (0, 2021-05-22)
data\PROTEINS\processed (0, 2021-05-22)
data\PROTEINS\processed\data.pt (2837541, 2021-05-22)
data\PROTEINS\processed\pre_filter.pt (431, 2021-05-22)
data\PROTEINS\processed\pre_transform.pt (431, 2021-05-22)
data\PROTEINS\raw (0, 2021-05-22)
data\PROTEINS\raw\PROTEINS_A.txt (2022990, 2021-05-22)
data\PROTEINS\raw\PROTEINS_graph_indicator.txt (169673, 2021-05-22)
data\PROTEINS\raw\PROTEINS_graph_labels.txt (2226, 2021-05-22)
data\PROTEINS\raw\PROTEINS_node_attributes.txt (478190, 2021-05-22)
data\PROTEINS\raw\PROTEINS_node_labels.txt (86942, 2021-05-22)
main.py (4825, 2021-05-22)
model.jpg (155252, 2021-05-22)

README for dataset PROTEINS === Usage === This folder contains the following comma separated text files (replace DS by the name of the dataset): n = total number of nodes m = total number of edges N = number of graphs (1) DS_A.txt (m lines) sparse (block diagonal) adjacency matrix for all graphs, each line corresponds to (row, col) resp. (node_id, node_id) (2) DS_graph_indicator.txt (n lines) column vector of graph identifiers for all nodes of all graphs, the value in the i-th line is the graph_id of the node with node_id i (3) DS_graph_labels.txt (N lines) class labels for all graphs in the dataset, the value in the i-th line is the class label of the graph with graph_id i (4) DS_node_labels.txt (n lines) column vector of node labels, the value in the i-th line corresponds to the node with node_id i There are OPTIONAL files if the respective information is available: (5) DS_edge_labels.txt (m lines; same size as DS_A_sparse.txt) labels for the edges in DS_A_sparse.txt (6) DS_edge_attributes.txt (m lines; same size as DS_A.txt) attributes for the edges in DS_A.txt (7) DS_node_attributes.txt (n lines) matrix of node attributes, the comma seperated values in the i-th line is the attribute vector of the node with node_id i (8) DS_graph_attributes.txt (N lines) regression values for all graphs in the dataset, the value in the i-th line is the attribute of the graph with graph_id i === Previous Use of the Dataset === Feragen, A., Kasenburg, N., Petersen, J., de Bruijne, M., Borgwardt, K.M.: Scalable kernels for graphs with continuous attributes. In: C.J.C. Burges, L. Bottou, Z. Ghahra- mani, K.Q. Weinberger (eds.) NIPS, pp. 216-224 (2013) Neumann, M., Garnett R., Bauckhage Ch., Kersting K.: Propagation Kernels: Efficient Graph Kernels from Propagated Information. Under review at MLJ. === References === K. M. Borgwardt, C. S. Ong, S. Schoenauer, S. V. N. Vishwanathan, A. J. Smola, and H. P. Kriegel. Protein function prediction via graph kernels. Bioinformatics, 21(Suppl 1):i47–i56, Jun 2005. P. D. Dobson and A. J. Doig. Distinguishing enzyme structures from non-enzymes without alignments. J. Mol. Biol., 330(4):771–783, Jul 2003.

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