Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels.
Enyan DaiWei JinHui LiuSuhang WangPublished in: CoRR (2022)
Keyphrases
- labeled graphs
- neural network
- graph representation
- graph theory
- graph matching
- edge weights
- gaussian graphical models
- directed acyclic
- graph theoretic
- directed graph
- graph structure
- label noise
- graph construction
- weighted graph
- graph databases
- bipartite graph
- series parallel
- random graphs
- graph theoretical
- graph clustering
- graph search
- graph model
- undirected graph
- graph classification
- graph mining
- graph structures
- pattern recognition
- adjacency matrix
- noisy environments
- densely connected
- structural pattern recognition
- graph partitioning
- graph data
- multi label
- pairwise
- graph patterns
- training data
- graph kernels
- spanning tree
- structured data
- neighborhood graph
- minimum spanning tree
- small world
- random walk
- directed acyclic graph
- adjacency graph
- graph isomorphism
- spectral clustering
- maximum common subgraph
- artificial neural networks
- active learning
- dense subgraphs
- polynomial time complexity
- graph transformation
- graph properties
- graph representations
- attributed graphs
- label propagation
- frequent subgraphs
- graph laplacian