Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications.
Kamilia ZaripovaLuca CosmoAnees KaziSeyed-Ahmad AhmadiMichael M. BronsteinNassir NavabPublished in: Medical Image Anal. (2023)
Keyphrases
- graph theory
- graph representation
- graph model
- weighted graph
- learning process
- graph theoretic
- graph construction
- graph properties
- adjacency matrix
- bipartite graph
- directed graph
- graph clustering
- graph structure
- graph search
- structural learning
- graph partitioning
- graph databases
- graph mining
- graph matching
- spanning tree
- random graphs
- connected components
- labeled graphs
- graph theoretical
- fully connected
- spectral embedding
- minimum spanning tree
- graph classification
- structured data
- random walk
- structural pattern recognition
- directed acyclic graph
- undirected graph
- edge weights
- subgraph isomorphism
- web graph
- directed acyclic
- graph layout
- graph isomorphism
- real world graphs
- adjacency graph
- dictionary learning
- graph patterns
- graph representations
- attributed graphs
- planar graphs
- graph structures
- bounded treewidth