HyperAggregation is a hypernetwork-based aggregation function for Graph Neural Networks. It uses a hypernetwork to dynamically generate weights in the size of the current neighborhood, which are then used to aggregate this neighborhood. This aggregation with the generated weights is done like an MLP-Mixer channel mixing over variable-sized vertex neighborhoods. We demonstrate HyperAggregation in two models, GraphHyperMixer is a model based on MLP-Mixer while GraphHyperConv is derived from a GCN but with a hypernetwork-based aggregation function. We perform experiments on diverse benchmark datasets for the vertex classification, graph classification, and graph regression tasks. The results show that HyperAggregation can be effectively used for homophilic and heterophilic datasets in both inductive and transductive settings. GraphHyperConv performs better than GraphHyperMixer and is especially strong in the transductive setting. On the heterophilic dataset Roman-Empire it reaches a new state of the art. On the graph-level tasks our models perform in line with similarly sized models. Ablation studies investigate the robustness against various hyperparameter choices. The implementation of HyperAggregation as well code to reproduce all experiments is available under https://github.com/Foisunt/HyperAggregation .

翻译：超聚合是一种基于超网络的图神经网络聚合函数。该方法利用超网络动态生成与当前邻域规模相匹配的权重，进而实现邻域聚合。这种基于生成权重的聚合过程类似于MLP-Mixer在可变规模顶点邻域上进行的通道混合操作。我们通过两个模型展示了超聚合的实现：GraphHyperMixer是基于MLP-Mixer构建的模型，而GraphHyperConv则源自GCN架构，但采用了基于超网络的聚合函数。我们在多种基准数据集上进行了顶点分类、图分类和图回归任务的实验。结果表明，超聚合在归纳式和传导式设置中均能有效适用于同配性与异配性数据集。GraphHyperConv的表现优于GraphHyperMixer，在传导式设置中表现尤为突出。在异配性数据集Roman-Empire上，该模型达到了新的最优性能。在图级任务中，我们的模型与同等规模模型表现相当。消融实验验证了模型对不同超参数选择的鲁棒性。超聚合的实现代码及所有实验复现代码已发布于https://github.com/Foisunt/HyperAggregation。