Network representation learning: a systematic literature review
B Li, D Pi - Neural Computing and Applications, 2020 - Springer
Omnipresent network/graph data generally have the characteristics of nonlinearity,
sparseness, dynamicity and heterogeneity, which bring numerous challenges to network …
sparseness, dynamicity and heterogeneity, which bring numerous challenges to network …
Hi-GCN: A hierarchical graph convolution network for graph embedding learning of brain network and brain disorders prediction
Purpose Recently, brain connectivity networks have been used for the classification of
neurological disorder, such as Autism Spectrum Disorders (ASD) or Alzheimer's disease …
neurological disorder, such as Autism Spectrum Disorders (ASD) or Alzheimer's disease …
Deep reinforcement learning guided graph neural networks for brain network analysis
Modern neuroimaging techniques enable us to construct human brains as brain networks or
connectomes. Capturing brain networks' structural information and hierarchical patterns is …
connectomes. Capturing brain networks' structural information and hierarchical patterns is …
Contrastive brain network learning via hierarchical signed graph pooling model
Recently, brain networks have been widely adopted to study brain dynamics, brain
development, and brain diseases. Graph representation learning techniques on brain …
development, and brain diseases. Graph representation learning techniques on brain …
Modeling dynamic characteristics of brain functional connectivity networks using resting-state functional MRI
Dynamic network analysis using resting-state functional magnetic resonance imaging (rs-
fMRI) provides a great insight into fundamentally dynamic characteristics of human brains …
fMRI) provides a great insight into fundamentally dynamic characteristics of human brains …
Feature extraction for incomplete data via low-rank tensor decomposition with feature regularization
MRI-derived brain networks have been widely used to understand functional and structural
interactions among brain regions, and factors that affect them, such as brain development …
interactions among brain regions, and factors that affect them, such as brain development …
Structural deep brain network mining
Mining from neuroimaging data is becoming increasingly popular in the field of healthcare
and bioinformatics, due to its potential to discover clinically meaningful structure patterns …
and bioinformatics, due to its potential to discover clinically meaningful structure patterns …
Consistent connectome landscape mining for cross-site brain disease identification using functional MRI
Many human brain disorders are associated with characteristic alterations in functional
connectivity of the brain. A lot of efforts have been devoted to mining disease-related …
connectivity of the brain. A lot of efforts have been devoted to mining disease-related …
Kernelized support tensor machines
In the context of supervised tensor learning, preserving the structural information and
exploiting the discriminative nonlinear relationships of tensor data are crucial for improving …
exploiting the discriminative nonlinear relationships of tensor data are crucial for improving …