SpanGNN: Towards Memory-Efficient Graph Neural Networks via Spanning Subgraph Training.
Xizhi GuHongzheng LiShihong GaoXinyan ZhangLei ChenYingxia ShaoPublished in: CoRR (2024)
Keyphrases
- memory efficient
- graph search
- neural network
- training process
- training algorithm
- feedforward neural networks
- graph databases
- external memory
- graph properties
- graph mining
- neural network structure
- subgraph isomorphism
- graph data
- multi layer perceptron
- multiple sequence alignment
- backpropagation algorithm
- query graph
- graph classification
- labeled graphs
- structured data
- feed forward neural networks
- back propagation
- pattern recognition
- subgraph mining
- iterative deepening
- frequent subgraph mining
- subgraph matching
- maximum weight
- maximum matching
- graph theory
- multilayer perceptron
- graph matching
- training data
- weighted graph
- similarity graph
- spanning tree
- bipartite graph
- connected components
- random walk
- artificial neural networks
- search algorithm