Scalable Graph Neural Networks for Heterogeneous Graphs.
Lingfan YuJiajun ShenJinyang LiAdam LererPublished in: CoRR (2020)
Keyphrases
- neural network
- graph representation
- graph theory
- directed graph
- graph structure
- graph construction
- labeled graphs
- graph databases
- graph matching
- weighted graph
- graph theoretic
- graph classification
- pattern recognition
- graph mining
- graph properties
- adjacency matrix
- structural pattern recognition
- graph model
- graph structures
- subgraph isomorphism
- graph partitioning
- spanning tree
- series parallel
- graph data
- random graphs
- graph theoretical
- graph clustering
- bipartite graph
- graph representations
- graph transformation
- graph kernels
- graph search
- graph mining algorithms
- reachability queries
- undirected graph
- dense subgraphs
- dynamic graph
- maximal cliques
- neighborhood graph
- inexact graph matching
- random walk
- edge weights
- graph drawing
- real world graphs
- attributed graphs
- maximum independent set
- artificial neural networks
- graph layout
- vertex set
- graph isomorphism
- maximum common subgraph
- minimum spanning tree
- directed acyclic
- connected dominating set
- connected graphs
- polynomial time complexity
- average degree
- web graph
- directed acyclic graph
- maximum clique
- topological information
- query graph
- association graph
- disk resident
- strongly connected
- maximum cardinality
- structured data
- spectral decomposition
- social graphs
- finding the shortest path
- back propagation
- proximity graph
- connected components
- small world