Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmark

1 code implementation • 9 Mar 2024 • Xiaowei Qian, Zhimeng Guo, Jialiang Li, Haitao Mao, Bingheng Li, Suhang Wang, Yao Ma

These datasets are thoughtfully designed to include relevant graph structures and bias information crucial for the fair evaluation of models.

Benchmarking Fairness +1

Estimating Coronal Mass Ejection Mass and Kinetic Energy by Fusion of Multiple Deep-learning Models

no code implementations • 4 Dec 2023 • Khalid A. Alobaid, Yasser Abduallah, Jason T. L. Wang, Haimin Wang, Shen Fan, Jialiang Li, Huseyin Cavus, Vasyl Yurchyshyn

In this paper, we propose a new method, called DeepCME, to estimate two properties of CMEs, namely, CME mass and kinetic energy.

Towards Fair Graph Neural Networks via Graph Counterfactual

1 code implementation • 10 Jul 2023 • Zhimeng Guo, Jialiang Li, Teng Xiao, Yao Ma, Suhang Wang

Despite their great performance in modeling graphs, recent works show that GNNs tend to inherit and amplify the bias from training data, causing concerns of the adoption of GNNs in high-stake scenarios.

counterfactual Fairness +2

Limited Query Graph Connectivity Test

no code implementations • 25 Feb 2023 • Mingyu Guo, Jialiang Li, Aneta Neumann, Frank Neumann, Hung Nguyen

Given a source s and a destination t, we aim to test s-t connectivity by identifying either a path (consisting of only On edges) or a cut (consisting of only Off edges).

Reinforcement Learning (RL)

Practical Fixed-Parameter Algorithms for Defending Active Directory Style Attack Graphs

no code implementations • 25 Dec 2021 • Mingyu Guo, Jialiang Li, Aneta Neumann, Frank Neumann, Hung Nguyen

The other assumes a small number of splitting nodes (nodes with multiple out-going edges).

Management Tree Decomposition