Graph prolongation convolutional networks: explicitly multiscale machine learning on graphs with applications to modeling of cytoskeleton.
Cory B. ScottEric MjolsnessPublished in: Mach. Learn. Sci. Technol. (2021)
Keyphrases
- multiscale
- machine learning
- graph structures
- average degree
- graph theory
- graph representation
- highly connected
- weighted graph
- graph mining
- real world graphs
- edge weights
- graph layout
- small world
- graph construction
- graph structure
- graph databases
- fully connected
- graph matching
- random graphs
- community discovery
- graph clustering
- dynamic networks
- graph theoretic
- series parallel
- social graphs
- graph theoretical
- directed graph
- graph model
- subgraph isomorphism
- labeled graphs
- graph classification
- graph data
- complex networks
- undirected graph
- graph representations
- degree distribution
- protein interaction networks
- graph search
- betweenness centrality
- deep learning
- spanning tree
- graph partitioning
- real world networks
- adjacency matrix
- image processing
- structured data
- graph isomorphism
- bipartite graph
- minimum spanning tree
- dense subgraphs
- maximal cliques
- directed edges
- graph properties
- structural pattern recognition
- random graph models
- learning algorithm
- graph kernels
- biological networks
- random walk
- community detection
- restricted boltzmann machine
- shortest path
- network analysis
- maximum common subgraph
- social networks
- graph mining algorithms
- image segmentation
- pattern mining
- bounded degree
- densely connected
- directed acyclic graph
- graph patterns
- functional modules
- maximum clique
- query graph