Open Graph Benchmark: Datasets for Machine Learning on Graphs.
Weihua HuMatthias FeyMarinka ZitnikYuxiao DongHongyu RenBowen LiuMichele CatastaJure LeskovecPublished in: NeurIPS (2020)
Keyphrases
- benchmark datasets
- machine learning
- graph representation
- graph structure
- graph theory
- weighted graph
- graph matching
- adjacency matrix
- graph mining
- graph construction
- graph model
- directed graph
- labeled graphs
- graph theoretic
- graph databases
- graph structures
- graph classification
- graph clustering
- series parallel
- graph theoretical
- graph partitioning
- random graphs
- graph search
- subgraph isomorphism
- bipartite graph
- graph kernels
- graph properties
- machine learning methods
- uci repository
- dynamic graph
- undirected graph
- reachability queries
- uci machine learning repository
- graph isomorphism
- minimum spanning tree
- graph data
- graph representations
- machine learning algorithms
- real world graphs
- planar graphs
- ensemble methods
- semi supervised learning
- spanning tree
- structural pattern recognition
- decision trees
- finding the shortest path
- adjacency graph
- maximum clique
- learning algorithm
- feature selection
- edge weights
- connected components
- maximum common subgraph
- attributed graphs
- terms of classification accuracy
- structured data
- connected graphs
- dense subgraphs
- polynomial time complexity
- maximal cliques
- query graph
- web graph
- text classification
- supervised learning
- support vector machine
- graph patterns
- graph layout
- knn
- neighborhood graph
- topological information
- community discovery