将图神经网络升级为能处理高阶关系的拓扑神经网络框架
TopoTune : A Framework for Generalized Combinatorial Complex Neural Networks
- 提出通用拓扑神经网络架构，可自动将图神经网络转为高阶结构模型
- 实验表明新模型性能优于或等同于现有方法，且参数更少
- 开源工具TopoTune让非专家也能轻松构建和训练这类模型
图神经网络（GNN）通过利用图对称性有效学习关系数据，但无法捕捉生物或社交网络中的多路交互。拓扑深度学习（TDL）通过建模高阶结构来解决此问题，组合复形神经网络（CCNNs）表现出强大表达能力，优于传统GNN。然而，当前TDL缺乏系统化开发框架，限制其应用。为此，我们提出广义组合复形神经网络（GCCNs），一个简单而强大的TDL模型家族，可将任意（图）神经网络系统性转化为对应的TDL版本。理论证明GCCNs涵盖并推广了CCNNs；大量实验显示，各类GCCNs在多种任务中表现与或优于CCNNs，且模型复杂度更低。为加速和普及TDL，我们推出轻量级工具TopoTune，支持定义、构建和训练GCCNs，具备前所未有的灵活性与易用性。
原文摘要 · Abstract (English)
Graph Neural Networks (GNNs) effectively learn from relational data by leveraging graph symmetries. However, many real-world systems -- such as biological or social networks -- feature multi-way interactions that GNNs fail to capture. Topological Deep Learning (TDL) addresses this by modeling and leveraging higher-order structures, with Combinatorial Complex Neural Networks (CCNNs) offering a general and expressive approach that has been shown to outperform GNNs. However, TDL lacks the principled and standardized frameworks that underpin GNN development, restricting its accessibility and applicability. To address this issue, we introduce Generalized CCNNs (GCCNs), a simple yet powerful family of TDL models that can be used to systematically transform any (graph) neural network into its TDL counterpart. We prove that GCCNs generalize and subsume CCNNs, while extensive experiments on a diverse class of GCCNs show that these architectures consistently match or outperform CCNNs, often with less model complexity. In an effort to accelerate and democratize TDL, we introduce TopoTune, a lightweight software for defining, building, and training GCCNs with unprecedented flexibility and ease.
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