Since the onset of the COVID-19 pandemic, there has been a growing interest in studying
epidemiological models. Traditional mechanistic models mathematically describe the …

Deep graph learning (DGL) has achieved remarkable progress in both business and
scientific areas ranging from finance and e-commerce, to drug and advanced material …

Benefiting from the intrinsic supervision information exploitation capability, contrastive
learning has achieved promising performance in the field of deep graph clustering recently …

Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …

The second volume of “Advancing Uncertain Combinatorics through Graphization,
Hyperization, and Uncertainization: Fuzzy, Neutrosophic, Soft, Rough, and Beyond” …

As training deep learning models on large dataset takes a lot of time and resources, it is
desired to construct a small synthetic dataset with which we can train deep learning models …

A cursory reading of the literature suggests that we have made a lot of progress in designing
effective adversarial defenses for Graph Neural Networks (GNNs). Yet, the standard …

Deep neural networks (DNNs) have been widely applied to various applications, including
image classification, text generation, audio recognition, and graph data analysis. However …

Abstract Graph Neural Networks (GNNs) have shown exceptional performance in the task of
link prediction. Despite their effectiveness, the high latency brought by non-trivial …

Graph theory, a branch of mathematics, explores relationships among entities using vertices
and edges. To address the uncertainties inherent in real-world networks, Uncertain Graph …