Interpretable graph neural networks for connectome-based brain disorder analysis
Human brains lie at the core of complex neurobiological systems, where the neurons,
circuits, and subsystems interact in enigmatic ways. Understanding the structural and …
circuits, and subsystems interact in enigmatic ways. Understanding the structural and …
On positional and structural node features for graph neural networks on non-attributed graphs
Graph neural networks (GNNs) have been widely used in various graph-related problems
such as node classification and graph classification, where the superior performance is …
such as node classification and graph classification, where the superior performance is …
A-GCL: Adversarial graph contrastive learning for fMRI analysis to diagnose neurodevelopmental disorders
Accurate diagnosis of neurodevelopmental disorders is a challenging task due to the time-
consuming cognitive tests and potential human bias in clinics. To address this challenge, we …
consuming cognitive tests and potential human bias in clinics. To address this challenge, we …
[HTML][HTML] A comprehensive survey of complex brain network representation
Recent years have shown great merits in utilizing neuroimaging data to understand brain
structural and functional changes, as well as its relationship to different neurodegenerative …
structural and functional changes, as well as its relationship to different neurodegenerative …
Data-efficient brain connectome analysis via multi-task meta-learning
Brain networks characterize complex connectivities among brain regions as graph
structures, which provide a powerful means to study brain connectomes. In recent years …
structures, which provide a powerful means to study brain connectomes. In recent years …
Learning on Multimodal Graphs: A Survey
Multimodal data pervades various domains, including healthcare, social media, and
transportation, where multimodal graphs play a pivotal role. Machine learning on multimodal …
transportation, where multimodal graphs play a pivotal role. Machine learning on multimodal …
Neurograph: Benchmarks for graph machine learning in brain connectomics
Abstract Machine learning provides a valuable tool for analyzing high-dimensional
functional neuroimaging data, and is proving effective in predicting various neurological …
functional neuroimaging data, and is proving effective in predicting various neurological …
Learning task-aware effective brain connectivity for fmri analysis with graph neural networks
Functional magnetic resonance imaging (fMRI) has become one of the most common
imaging modalities for brain function analysis. Recently, graph neural networks (GNN) have …
imaging modalities for brain function analysis. Recently, graph neural networks (GNN) have …
R-mixup: Riemannian mixup for biological networks
Biological networks are commonly used in biomedical and healthcare domains to effectively
model the structure of complex biological systems with interactions linking biological entities …
model the structure of complex biological systems with interactions linking biological entities …
Cf-gode: Continuous-time causal inference for multi-agent dynamical systems
Multi-agent dynamical systems refer to scenarios where multiple units (aka agents) interact
with each other and evolve collectively over time. For instance, people's health conditions …
with each other and evolve collectively over time. For instance, people's health conditions …