1 citations · 2 across the 6 of their papers we have counts for
5 papers · 1 filter
Overcoming Class Imbalance: Unified GNN Learning with Structural and Semantic Connectivity Representations
Abdullah Alchihabi, Hao Yan, Yuhong Guo
Class imbalance is pervasive in real-world graph datasets, where the majority of annotated nodes belong to a small set of classes (majority classes), leaving many other classes (mi…
Adaptive Weighted Co-Learning for Cross-Domain Few-Shot Learning
Abdullah Alchihabi, Marzi Heidari, Yuhong Guo
Due to the availability of only a few labeled instances for the novel target prediction task and the significant domain shift between the well annotated source domain and the targe…
Efficient Low-Rank GNN Defense Against Structural Attacks
Abdullah Alchihabi, Qing En, Yuhong Guo
Graph Neural Networks (GNNs) have been shown to possess strong representation abilities over graph data. However, GNNs are vulnerable to adversarial attacks, and even minor perturb…
GDM: Dual Mixup for Graph Classification with Limited Supervision
Abdullah Alchihabi, Yuhong Guo
Graph Neural Networks (GNNs) require a large number of labeled graph samples to obtain good performance on the graph classification task. The performance of GNNs degrades significa…
Dual GNNs: Graph Neural Network Learning with Limited Supervision
Abdullah Alchihabi, Yuhong Guo
Graph Neural Networks (GNNs) require a relatively large number of labeled nodes and a reliable/uncorrupted graph connectivity structure in order to obtain good performance on the s…