X-RLflow: Graph Reinforcement Learning for Neural Network Subgraphs Transformation.
Guoliang HeSean ParkerEiko YonekiPublished in: CoRR (2023)
Keyphrases
- neural network
- reinforcement learning
- graph mining
- graph properties
- graph databases
- labeled graphs
- graph structures
- frequent subgraph mining
- subgraph mining
- graph data
- graph theory
- back propagation
- subgraph isomorphism
- random walk
- query graph
- subgraph matching
- fault diagnosis
- connected subgraphs
- graph theoretic
- artificial neural networks
- function approximators
- function approximation
- reachability queries
- graph structure
- neural network model
- connected components
- graph patterns
- learning capabilities
- reinforcement learning algorithms
- frequent subgraphs
- maximum clique
- quasi cliques
- machine learning
- genetic algorithm
- graph model
- multi agent
- data structure
- state space
- maximal cliques
- pattern recognition
- knn
- topological information
- structured data
- directed graph
- weighted graph
- directed acyclic graph
- graph partitioning
- pattern mining
- spanning tree
- graph matching
- recurrent neural networks
- fuzzy logic
- graph clustering
- regular expressions
- dynamic programming
- learning process
- dense subgraphs
- temporal difference
- model free