SES: Bridging the Gap Between Explainability and Prediction of Graph Neural Networks.
Zhenhua HuangKunhao LiShaojie WangZhaohong JiaWentao ZhuSharad MehrotraPublished in: ICDE (2024)
Keyphrases
- neural network
- prediction accuracy
- pattern recognition
- artificial neural networks to predict
- prediction model
- protein function prediction
- connected components
- graph theory
- bipartite graph
- random walk
- self organizing maps
- genetic algorithm
- multilayer perceptron
- graph representation
- graph model
- prediction error
- graph structure
- decision trees
- neural networks and support vector machines
- feed forward
- graph theoretic
- directed graph
- fault diagnosis
- regression model
- graph based algorithm
- chaotic time series
- recurrent neural networks
- spanning tree
- graph databases
- graph mining
- training process
- fuzzy systems
- multi layer
- graph matching
- neural network model
- structured data
- back propagation
- fuzzy logic
- artificial neural networks
- machine learning