CSP: An Efficient Baseline for Learning on Large-Scale Structured Data
Keywords: Hypergraph representation learning, Hypergraph convolution, Label propagation, Model complexity, Naive Bayes
TL;DR: This paper proposes Convolutional Signal Propagation (CSP), a non-parametric simple and scalable method that natively operates on hypergraphs and can be implemented with just a few lines of code.
Abstract: 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.
Primary Area: learning on graphs and other geometries & topologies
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Submission Number: 7049
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