A review of graph neural networks in epidemic modeling
Since the onset of the COVID-19 pandemic, there has been a growing interest in studying
epidemiological models. Traditional mechanistic models mathematically describe the …
epidemiological models. Traditional mechanistic models mathematically describe the …
Federated learning for generalization, robustness, fairness: A survey and benchmark
Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …
collaboration among different parties. Recently, with the popularity of federated learning, an …
Disentangled contrastive learning for fair graph representations
Abstract Graph Neural Networks (GNNs) play a key role in efficiently learning node
representations of graph-structured data through message passing, but their predictions are …
representations of graph-structured data through message passing, but their predictions are …
Deep reinforcement learning for mobile robot path planning
H Liu, Y Shen, S Yu, Z Gao, T Wu - arXiv preprint arXiv:2404.06974, 2024 - arxiv.org
Path planning is an important problem with the the applications in many aspects, such as
video games, robotics etc. This paper proposes a novel method to address the problem of …
video games, robotics etc. This paper proposes a novel method to address the problem of …
FedAS: Bridging Inconsistency in Personalized Federated Learning
Abstract Personalized Federated Learning (PFL) is primarily designed to provide
customized models for each client to better fit the non-iid distributed client data which is a …
customized models for each client to better fit the non-iid distributed client data which is a …
Resisting over-smoothing in graph neural networks via dual-dimensional decoupling
Graph Neural Networks (GNNs) are widely employed to derive meaningful node
representations from graphs. Despite their success, deep GNNs frequently grapple with the …
representations from graphs. Despite their success, deep GNNs frequently grapple with the …
Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graph
Effective epidemic forecasting is critical for public health strategies and efficient medical
resource allocation, especially in the face of rapidly spreading infectious diseases. However …
resource allocation, especially in the face of rapidly spreading infectious diseases. However …
Openfgl: A comprehensive benchmarks for federated graph learning
Federated graph learning (FGL) has emerged as a promising distributed training paradigm
for graph neural networks across multiple local systems without direct data sharing. This …
for graph neural networks across multiple local systems without direct data sharing. This …
Distance Aware Risk Minimization for Domain Generalization in Machine Fault Diagnosis
Industrial Internet of Things (IIoT) connects machines, and it is important to build intelligent
models to prevent machine failures by identifying incipient faults. To develop intelligent fault …
models to prevent machine failures by identifying incipient faults. To develop intelligent fault …
Dual-channel meta-federated graph learning with robust aggregation and privacy enhancement
Graph neural networks (GNNs) are effective for graph-based node classification tasks, such
as data mining and recommendation systems. Combining federated learning (FL) with GNN …
as data mining and recommendation systems. Combining federated learning (FL) with GNN …