V-InFoR: A Robust Graph Neural Networks Explainer for Structurally Corrupted Graphs.
Senzhang WangJun YinChaozhuo LiXing XieJianxin WangPublished in: NeurIPS (2023)
Keyphrases
- neural network
- graph theory
- graph representation
- weighted graph
- directed graph
- graph matching
- graph structure
- graph construction
- graph theoretic
- graph databases
- graph mining
- labeled graphs
- adjacency matrix
- graph partitioning
- graph structures
- graph classification
- graph model
- bipartite graph
- graph isomorphism
- subgraph isomorphism
- structured data
- graph properties
- graph search
- undirected graph
- dynamic graph
- structural pattern recognition
- series parallel
- adjacency graph
- pattern recognition
- minimum spanning tree
- small world
- graph patterns
- graph drawing
- disk resident
- artificial neural networks
- random walk
- back propagation
- maximum common subgraph
- directed acyclic graph
- attributed graphs
- topological information
- spanning tree
- graph theoretical
- connected graphs
- graph clustering
- random graphs
- graph kernels
- maximum independent set
- evolving graphs
- inexact graph matching
- connected dominating set
- proximity graph
- polynomial time complexity
- maximal cliques
- graph embedding
- neighborhood graph
- graph data
- link prediction
- social networks