260 citations · 1.2k across the 75 of their papers we have counts for
10 papers · 1 filter
Structure-Preserving Graph Kernel for Brain Network Classification
Jun Yu, Zhaoming Kong, Aditya Kendre +5
This paper presents a novel graph-based kernel learning approach for connectome analysis. Specifically, we demonstrate how to leverage the naturally available structure within the…
Multiplex Graph Networks for Multimodal Brain Network Analysis
Zhaoming Kong, Lichao Sun, Hao Peng +3
In this paper, we propose MGNet, a simple and effective multiplex graph convolutional network (GCN) model for multimodal brain network analysis. The proposed method integrates tens…
BrainNNExplainer: An Interpretable Graph Neural Network Framework for Brain Network based Disease Analysis
Hejie Cui, Wei Dai, Yanqiao Zhu +3
Interpretable brain network models for disease prediction are of great value for the advancement of neuroscience. GNNs are promising to model complicated network data, but they are…
Multi-VAE: Learning Disentangled View-common and View-peculiar Visual Representations for Multi-view Clustering
Jie Xu, Yazhou Ren, Huayi Tang +4
Multi-view clustering, a long-standing and important research problem, focuses on mining complementary information from diverse views. However, existing works often fuse multiple v…
Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis
Yanqiao Zhu, Hejie Cui, Lifang He +2
Multimodal brain networks characterize complex connectivities among different brain regions from both structural and functional aspects and provide a new means for mental disease a…
Federated Multi-View Learning for Private Medical Data Integration and Analysis
Sicong Che, Hao Peng, Lichao Sun +2
Along with the rapid expansion of information technology and digitalization of health data, there is an increasing concern on maintaining data privacy while garnering the benefits…
FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks
Chaoyang He, Keshav Balasubramanian, Emir Ceyani +11
Graph Neural Network (GNN) research is rapidly growing thanks to the capacity of GNNs in learning distributed representations from graph-structured data. However, centralizing a ma…
FedMood: Federated Learning on Mobile Health Data for Mood Detection
Xiaohang Xu, Hao Peng, Lichao Sun +3
Depression is one of the most common mental illness problems, and the symptoms shown by patients are not consistent, making it difficult to diagnose in the process of clinical prac…
Heterogeneous Similarity Graph Neural Network on Electronic Health Records
Zheng Liu, Xiaohan Li, Hao Peng +2
Mining Electronic Health Records (EHRs) becomes a promising topic because of the rich information they contain. By learning from EHRs, machine learning models can be built to help…
SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism
Qingyun Sun, Jianxin Li, Hao Peng +4
Graph representation learning has attracted increasing research attention. However, most existing studies fuse all structural features and node attributes to provide an overarching…