3 citations · 4 across the 6 of their papers we have counts for
6 papers
M-LINKX: Multiview Graph Learning for Brain Cognitive Disease Detection
An Phan, Yufei Jin, Xingquan Zhu
Electroencephalogram (EEG) is a non-invasive and relatively low-cost procedure that measures brain electricity for the detection of cognitive diseases. EEG-based classification of…
COMBA: Cross Batch Aggregation for Learning Large Graphs with Context Gating State Space Models
Jiajun Shen, Yufei Jin, Yi He +1
State space models (SSMs) have recently emerged for modeling long-range dependency in sequence data, with much simplified computational costs than modern alternatives, such as tran…
LHGEL: Large Heterogeneous Graph Ensemble Learning using Batch View Aggregation
Jiajun Shen, Yufei Jin, Yi He +1
Learning from large heterogeneous graphs presents significant challenges due to the scale of networks, heterogeneity in node and edge types, variations in nodal features, and compl…
HGEN: Heterogeneous Graph Ensemble Networks
Jiajun Shen, Yufei Jin, Yi He +1
This paper presents HGEN that pioneers ensemble learning for heterogeneous graphs. We argue that the heterogeneity in node types, nodal features, and local neighborhood topology po…
Topology-aware Neural Flux Prediction Guided by Physics
Haoyang Jiang, Jindong Wang, Xingquan Zhu +1
Graph Neural Networks (GNNs) often struggle in preserving high-frequency components of nodal signals when dealing with directed graphs. Such components are crucial for modeling flo…
Oversmoothing Alleviation in Graph Neural Networks: A Survey and Unified View
Yufei Jin, Xingquan Zhu
Oversmoothing is a common challenge in learning graph neural networks (GNN), where, as layers increase, embedding features learned from GNNs quickly become similar or indistinguish…