用霍奇分解融合顶点边特征，提升图分类精度
Graph Classification Gaussian Processes via Hodgelet Spectral Features
- 基于霍奇分解提取顶点与边的谱特征
- 支持同时利用顶点和边特征进行分类
- 适合需要细粒度结构建模的图任务
图分类问题是机器学习中的常见任务。尽管通常使用图神经网络或图核方法，但可通过将图域的空间特征转换为欧氏域的谱特征，并作为经典核函数的输入，来应用高斯过程。然而，当前方法仅考虑顶点特征，而部分图数据集还包含边特征。本文提出一种基于高斯过程的分类算法，可同时利用顶点和边特征。此外，借助霍奇分解，更充分捕捉顶点与边特征的复杂结构，对多种任务具有优势。
原文摘要 · Abstract (English)
The problem of classifying graphs is ubiquitous in machine learning. While it is standard to apply graph neural networks or graph kernel methods, Gaussian processes can be employed by transforming spatial features from the graph domain into spectral features in the Euclidean domain, and using them as the input points of classical kernels. However, this approach currently only takes into account features on vertices, whereas some graph datasets also support features on edges. In this work, we present a Gaussian process-based classification algorithm that can leverage one or both vertex and edges features. Furthermore, we take advantage of the Hodge decomposition to better capture the intricate richness of vertex and edge features, which can be beneficial on diverse tasks.
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