DD生物数据集 图神经网络

README for dataset DD === 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 === Description === D&D is a dataset of 1178 protein structures (Dobson and Doig, 2003). Each protein is represented by a graph, in which the nodes are amino acids and two nodes are connected by an edge if they are less than 6 Angstroms apart. The prediction task is to classify the protein structures into enzymes and non-enzymes. === Previous Use of the Dataset === Neumann, M., Garnett R., Bauckhage Ch., Kersting K.: Propagation Kernels: Efficient Graph Kernels from Propagated Information. Under review at MLJ. Neumann, M., Patricia, N., Garnett, R., Kersting, K.: Efficient Graph Kernels by Randomization. In: P.A. Flach, T.D. Bie, N. Cristianini (eds.) ECML/PKDD, Notes in Computer Science, vol. 7523, pp. 378-393. Springer (2012). Shervashidze, N., Schweitzer, P., van Leeuwen, E., Mehlhorn, K., Borgwardt, K.: Weisfeiler-Lehman Graph Kernels. Journal of Machine Learning Research 12, 2539-2561 (2011) === References === P. D. Dobson and A. J. Doig. Distinguishing enzyme structures from non-enzymes without alignments. J. Mol. Biol., 330(4):771–783, Jul 2003.