Traditional methods like Graph Convolutional Networks (GCNs) face challenges
with limited data and class imbalance, leading to suboptimal performance in
graph classification tasks during toxicity prediction of molecules as a whole.
To address these issues, we harness the power of Graph Isomorphic Networks,
Multi Headed Attention and Free Large-scale Adversarial Augmentation separately
on Graphs for precisely capturing the structural data of molecules and their
toxicological properties. Additionally, we incorporate Few-Shot Learning to
improve the model's generalization with limited annotated samples. Extensive
experiments on a diverse toxicology dataset demonstrate that our method
achieves an impressive state-of-art AUC-ROC value of 0.816, surpassing the
baseline GCN model by 11.4%. This highlights the significance of our proposed
methodology and Few Shot Learning in advancing Toxic Molecular Classification,
with the potential to enhance drug discovery and environmental risk assessment
processes.

采用图卷积网络（GCNs）、图同构网络、多头注意力、大规模对抗增强与少样本学习，本研究在毒性分子的图分类任务中对分子的结构数据与毒理学特性进行精确捕捉。实验表明，与基线 GCN 模型相比，本方法在多样的毒理学数据集上实现了 0.816 的令人瞩目的 AUC-ROC 值，超过基线模型 11.4%，突显了所提出方法和少样本学习在推进毒性分子分类方面的重要性，有望提高药物发现和环境风险评估流程。

使用图神经网络和小样本学习进行毒性分子分类的基准测试

Benchmarking Toxic Molecule Classification using Graph Neural Networks  and Few Shot Learning

Ensuring electricity grid reliability becomes increasingly challenging with
the shift towards renewable energy and declining conventional capacities.
Distribution System Operators (DSOs) aim to achieve grid reliability by
verifying the n-1 principle, ensuring continuous operation in case of component
failure. Electricity networks' complex graph-based data holds crucial
information for n-1 assessment: graph structure and data about stations/cables.
Unlike traditional machine learning methods, Graph Neural Networks (GNNs)
directly handle graph-structured data. This paper proposes using Graph
Isomorphic Networks (GINs) for n-1 assessments in medium voltage grids. The GIN
framework is designed to generalise to unseen grids and utilise graph structure
and data about stations/cables. The proposed GIN approach demonstrates faster
and more reliable grid assessments than a traditional mathematical optimisation
approach, reducing prediction times by approximately a factor of 1000. The
findings offer a promising approach to address computational challenges and
enhance the reliability and efficiency of energy grid assessments.

利用图同构网络（GIN）进行中压网的 1-n 评估，结果显示相较于传统的数学优化方法，GIN 方法具有更快、更可靠的评估速度，减少了大约 1000 倍的预测时间，为解决计算挑战、提高能源网络评估的可靠性和效率提供了一种有前景的方法。