A graph convolution network with subgraph embedding for mutagenic prediction in aromatic hydrocarbons.
Hyung-Jun MoonSeok-Jun BuSung-Bae ChoPublished in: Neurocomputing (2023)
Keyphrases
- graph mining
- random walk
- graph databases
- connected subgraphs
- prediction accuracy
- graph embedding
- fully connected
- graph properties
- graph data
- edge weights
- spanning tree
- prediction model
- network structure
- wireless sensor networks
- citation networks
- labeled graphs
- graph mining algorithms
- maximum matching
- frequent subgraph mining
- graphical representation
- directed acyclic graph
- link prediction
- small world
- random graphs
- prediction error
- maximum weight
- weighted graph
- bipartite graph
- vector space
- dimensionality reduction
- nodes of a graph
- neural network