TE-HI-GCN: An Ensemble of Transfer Hierarchical Graph Convolutional Networks for Disorder Diagnosis.
Lanting LiHao JiangGuangqi WenPeng CaoMingYi XuXiaoli LiuJinzhu YangOsmar R. ZaïanePublished in: Neuroinformatics (2022)
Keyphrases
- average degree
- directed edges
- edge weights
- small world
- random walk
- traffic engineering
- directed graph
- graph theory
- graph structures
- neural network ensemble
- model based diagnosis
- fully connected
- network structure
- bipartite graph
- medical diagnosis
- structural model
- protein interaction networks
- dynamic networks
- graph representation
- overlapping communities
- training data
- feature selection
- fault diagnosis
- weighted graph
- complex networks
- shortest path
- connected components
- sparse coding
- coarse to fine
- graph structure
- graph databases
- directed acyclic graph
- network analysis
- ensemble methods
- training set
- structured data
- degree distribution
- citation networks
- network architecture
- clustering coefficient
- community discovery
- quality of service
- network size
- transfer learning
- ensemble learning
- spanning tree