4 papers
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…
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…