Solving the AC optimal power flow problem (AC-OPF) is critical to the
efficient and safe planning and operation of power grids. Small efficiency
improvements in this domain have the potential to lead to billions of dollars
of cost savings, and significant reductions in emissions from fossil fuel
generators. Recent work on data-driven solution methods for AC-OPF shows the
potential for large speed improvements compared to traditional solvers;
however, no large-scale open datasets for this problem exist. We present the
largest readily-available collection of solved AC-OPF problems to date. This
collection is orders of magnitude larger than existing readily-available
datasets, allowing training of high-capacity data-driven models. Uniquely, it
includes topological perturbations - a critical requirement for usage in
realistic power grid operations. We hope this resource will spur the community
to scale research to larger grid sizes with variable topology.

交流最佳功率流问题是电网规划和运行的高效且安全的关键。最近关于基于数据驱动的交流最佳功率流问题解决方法的研究表明，与传统解算器相比，其潜在的速度提升巨大。然而，目前尚无大规模开放数据集可供该问题使用。我们提供了迄今最大规模的可用解决交流最佳功率流问题的收集。这一收集比现有的可用数据集大数个数量级，可用于训练高容量的数据驱动模型。独特之处在于它包括拓扑扰动 - 这是在实际电网操作中的一个关键要求。我们希望这一资源将推动社区将研究拓展到具有可变拓扑的更大网格规模。

OPFData：带有拓扑扰动的交流最优电力流大规模数据集

OPFData: Large-scale datasets for AC optimal power flow with topological  perturbations

Graph neural networks (GNNs) have been extensively employed in node
classification. Nevertheless, recent studies indicate that GNNs are vulnerable
to topological perturbations, such as adversarial attacks and edge disruptions.
Considerable efforts have been devoted to mitigating these challenges. For
example, pioneering Bayesian methodologies, including GraphSS and LlnDT,
incorporate Bayesian label transitions and topology-based label sampling to
strengthen the robustness of GNNs. However, GraphSS is hindered by slow
convergence, while LlnDT faces challenges in sparse graphs. To overcome these
limitations, we propose a novel label inference framework, TraTopo, which
combines topology-driven label propagation, Bayesian label transitions, and
link analysis via random walks. TraTopo significantly surpasses its
predecessors on sparse graphs by utilizing random walk sampling, specifically
targeting isolated nodes for link prediction, thus enhancing its effectiveness
in topological sampling contexts. Additionally, TraTopo employs a shortest-path
strategy to refine link prediction, thereby reducing predictive overhead and
improving label inference accuracy. Empirical evaluations highlight TraTopo's
superiority in node classification, significantly exceeding contemporary GCN
models in accuracy.

图神经网络（GNN）在节点分类中得到广泛应用，但最近的研究表明 GNN 对拓扑扰动如对抗攻击和边缘中断很容易受到攻击，为了克服这些问题，我们提出了一种新的标签推断框架 TraTopo，它结合了基于拓扑的标签传播、贝叶斯标签转换和随机游走的链接分析，并在稀疏图上超过了现有的 GCN 模型，在节点分类中表现出更高的准确性。

增强稀疏图中图神经网络对拓扑变动的鲁棒性

Enhancing the Resilience of Graph Neural Networks to Topological  Perturbations in Sparse Graphs

Analyzing the stability of graph neural networks (GNNs) under topological
perturbations is key to understanding their transferability and the role of
each architecture component. However, stability has been investigated only for
particular architectures, questioning whether it holds for a broader spectrum
of GNNs or only for a few instances. To answer this question, we study the
stability of EdgeNet: a general GNN framework that unifies more than twenty
solutions including the convolutional and attention-based classes, as well as
graph isomorphism networks and hybrid architectures. We prove that all GNNs
within the EdgeNet framework are stable to topological perturbations. By
studying the effect of different EdgeNet categories on the stability, we show
that GNNs with fewer degrees of freedom in their parameter space, linked to a
lower representational capacity, are more stable. The key factor yielding this
trade-off is the eigenvector misalignment between the EdgeNet parameter
matrices and the graph shift operator. For example, graph convolutional neural
networks that assign a single scalar per signal shift (hence, with a perfect
alignment) are more stable than the more involved node or edge-varying
counterparts. Extensive numerical results corroborate our theoretical findings
and highlight the role of different architecture components in the trade-off.

验证了在图神经网络中引入拓扑扰动的稳定性，针对不同结构的 EdgeNet 框架证明其稳定性，以及参数空间的自由度与代表能力之间的关系和边缘网络参数矩阵与图移动算子的特征向量错配程度对稳定性的影响。