1 citations · 2 across the 8 of their papers we have counts for
8 papers
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…
Adaptive Parametric Prototype Learning for Cross-Domain Few-Shot Classification
Marzi Heidari, Abdullah Alchihabi, Qing En +1
Cross-domain few-shot classification induces a much more challenging problem than its in-domain counterpart due to the existence of domain shifts between the training and test task…
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…
On the Brain Networks of Complex Problem Solving
Abdullah Alchihabi, Omer Ekmekci, Baran B. Kivilcim +2
Complex problem solving is a high level cognitive process which has been thoroughly studied over the last decade. The Tower of London (TOL) is a task that has been widely used to s…
Encoding Multi-Resolution Brain Networks Using Unsupervised Deep Learning
Arash Rahnama, Abdullah Alchihabi, Vijay Gupta +2
The main goal of this study is to extract a set of brain networks in multiple time-resolutions to analyze the connectivity patterns among the anatomic regions for a given cognitive…