GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity Interactions.
Hanchen WangDefu LianYing ZhangLu QinXuemin LinPublished in: CoRR (2020)
Keyphrases
- neural network
- graph representation
- structured data
- graph theory
- graph structure
- graph databases
- directed graph
- weighted graph
- graph construction
- graph structures
- graph matching
- adjacency matrix
- graph theoretic
- graph mining
- labeled graphs
- graph classification
- graph data
- graph model
- graph partitioning
- random graphs
- graph search
- graph clustering
- bipartite graph
- graph kernels
- graph transformation
- graph theoretical
- graph layout
- neighborhood graph
- series parallel
- graph representations
- dynamic graph
- spanning tree
- minimum spanning tree
- graph isomorphism
- directed acyclic
- connected graphs
- subgraph isomorphism
- undirected graph
- maximum common subgraph
- back propagation
- graph drawing
- graph properties
- artificial neural networks
- reachability queries
- connected components
- attributed graphs
- knn
- maximal cliques
- graph patterns
- maximum cardinality
- structural pattern recognition
- finding the shortest path
- adjacency graph
- planar graphs
- maximum clique
- real world graphs
- inexact graph matching
- web graph
- dense subgraphs
- topological information
- query graph
- social graphs
- edge weights
- proximity graph
- association graph
- random walk
- evolving graphs
- information extraction
- connected dominating set
- quasi cliques
- protein interaction
- polynomial time complexity
- frequent subgraphs
- bounded treewidth
- network properties