Convergence of Graph Neural Networks on Relatively Sparse Graphs.
Zhiyang WangLuana RuizAlejandro RibeiroPublished in: ACSSC (2023)
Keyphrases
- neural network
- graph representation
- directed acyclic
- graph theory
- graph structure
- graph matching
- weighted graph
- gaussian graphical models
- graph theoretic
- directed graph
- graph construction
- graph databases
- labeled graphs
- adjacency matrix
- structural pattern recognition
- graph search
- graph model
- densely connected
- graph structures
- graph clustering
- bipartite graph
- graph properties
- pattern recognition
- graph classification
- random graphs
- graph theoretical
- graph data
- spanning tree
- graph mining
- series parallel
- graph partitioning
- subgraph isomorphism
- weight update
- graph kernels
- dynamic graph
- graph isomorphism
- maximum common subgraph
- undirected graph
- inexact graph matching
- attributed graphs
- connected dominating set
- artificial neural networks
- graphical models
- graph embedding
- query graph
- graph layout
- frequent subgraphs
- neighborhood graph
- maximum clique
- graph transformation
- evolving graphs
- connected graphs
- graph patterns
- reachability queries
- sparse representation
- adjacency graph
- association graph
- maximal cliques
- maximum cardinality
- high dimensional
- quasi cliques
- pattern mining
- random walk
- polynomial time complexity
- social graphs
- small world
- real world graphs
- spectral clustering
- graph drawing
- directed acyclic graph
- massive graphs
- minimum spanning tree
- graph representations
- topological information
- planar graphs
- bounded treewidth