2018/08/07 by Giannis Nikolentzos, Nikolentzos, Giannis, Michalis Vazirgiannis +1 · 1 citation
Computer Science · Mathematics · #Advanced Graph Neural Networks #Artificial intelligence #Component (thermodynamics) #Computer science #Discrete mathematics #FOS: Computer and information sciences #Graph #Graph kernel #Kernel (algebra) #Kernel method #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Mathematics #Message passing #Parallel computing #Polynomial kernel #Support vector machine #Text and Document Classification Technologies #Theoretical computer science #Topic Modeling #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1808.02510
arxiv created 2018/08/07 · openalex publication_date 2018/08/07 · arxiv updated 2018/08/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Graph kernels have recently emerged as a promising approach for tackling the graph similarity and learning tasks at the same time. In this paper, we propose a general framework for designing graph kernels. The proposed framework capitalizes on the well-known message passing scheme on graphs. The kernels derived from the framework consist of two components. The first component is a kernel between vertices, while the second component is a kernel between graphs. The main idea behind the proposed framework is that the representations of the vertices are implicitly updated using an iterative procedure. Then, these representations serve as the building blocks of a kernel that compares pairs of graphs. We derive four instances of the proposed framework, and show through extensive experiments that these instances are competitive with state-of-the-art methods in various tasks.