提出简单高效的超图神经网络方法，可直接处理复杂关系数据。
Convolutional Signal Propagation: A Simple Scalable Algorithm for Hypergraphs
- 基于信号传播思想设计非参数化算法，天然适配二分图结构
- 在多个真实数据集上表现媲美主流方法，计算开销极低
- 代码简洁易实现，适合做超图任务基线，也适用于NLP等场景
过去十年间，图神经网络（GNNs）在图结构学习中发展迅速，但对更复杂的双分图（即超图）结构，如用户与电影的互动关系，难以直接应用。本文提出卷积式信号传播（CSP），一种非参数、简单且可扩展的方法，原生支持双分图（超图）操作，仅需几行代码即可实现。我们证明了CSP与标签传播、朴素贝叶斯及超图卷积网络之间存在理论关联。在跨多个领域的实际数据集上，针对检索和分类任务评估CSP，结果表明其性能具有竞争力，同时保持极低的计算复杂度，是超图节点分类与检索任务的理想基线。此外，尽管面向超图设计，CSP在通常不涉及超图的任务（如自然语言处理）中也表现出色。
原文摘要 · Abstract (English)
Last decade has seen the emergence of numerous methods for learning on graphs, particularly Graph Neural Networks (GNNs). These methods, however, are often not directly applicable to more complex structures like bipartite graphs (equivalent to hypergraphs), which represent interactions among two entity types (e.g. a user liking a movie). This paper proposes Convolutional Signal Propagation (CSP), a non-parametric simple and scalable method that natively operates on bipartite graphs (hypergraphs) and can be implemented with just a few lines of code. After defining CSP, we demonstrate its relationship with well-established methods like label propagation, Naive Bayes, and Hypergraph Convolutional Networks. We evaluate CSP against several reference methods on real-world datasets from multiple domains, focusing on retrieval and classification tasks. Our results show that CSP offers competitive performance while maintaining low computational complexity, making it an ideal first choice as a baseline for hypergraph node classification and retrieval. Moreover, despite operating on hypergraphs, CSP achieves good results in tasks typically not associated with hypergraphs, such as natural language processing.
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