图集转换器统一建模图集内局部与全局关系，提升预测精度。
Graph Set Transformer
- 通过层间交错传播与门控融合，同步处理图内结构与图集上下文。
- 在合成数据和三个真实任务上，参数相当下均优于基线模型。
- 适合需要图集上下文推理的分子反应、图像分类等场景。
我们提出图集转换器（Graph Set Transformer, GST），一种用于学习图集的神经网络架构，适用于依赖元素级上下文与局部结构的任务。现有方法如DeepSets和SetTransformer需先通过独立GNN提取图嵌入，导致特征提取与集合级上下文建模之间存在瓶颈。相比之下，GST在每一层中交错进行节点级特征传播与跨图上下文建模，并通过门控机制融合两层次信息。我们在一个可控的合成数据集上评估了GST，该数据集旨在分离集合条件下的结构推理能力；同时在三个真实数据基准上进行了测试，涵盖原子级反应中心识别、反应产率预测和图像分类任务。在相同参数预算下，GST在所有设置中均表现优于基线模型。架构消融实验强烈表明，局部信息与集合上下文的交错设计是性能提升的关键因素。
原文摘要 · Abstract (English)
We introduce the Graph Set Transformer (GST), a neural network architecture for learning on sets of graphs, designed for tasks in which per-element predictions depend on set-wide context as well as local structure. Existing architectures, including DeepSets and SetTransformer, require pre-encoded graph embeddings from a separate GNN, creating a bottleneck between feature extraction and set-level contextualisation. In contrast, GST interleaves node-level feature propagation and cross-graph contextual modelling at every layer, fusing the two levels of information through a gating mechanism. We evaluate GST on a controlled synthetic suite designed to isolate set-conditional structural reasoning and on three real-data benchmarks spanning per-atom reaction-centre identification, reaction yield prediction, and image classification. Under matched parameter budgets, GST performs better than the baselines across these settings. An architectural ablation strongly suggests that the interleaving of local and set context contributes substantially to this advantage.
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