Graph Kernels: A Survey

Abstract

Graph kernels have attracted a lot of attention during the last decade, and have evolved into a rapidly developing branch of learning on structured data. During the past 20 years, the considerable research activity that occurred in the field resulted in the development of dozens of graph kernels, each focusing on specific structural properties of graphs. Graph kernels have proven successful in a wide range of domains, ranging from social networks to bioinformatics. The goal of this survey is to provide a unifying view of the literature on graph kernels. In particular, we present a comprehensive overview of a wide range of graph kernels. Furthermore, we perform an experimental evaluation of several of those kernels on publicly available datasets, and provide a comparative study. Finally, we discuss key applications of graph kernels, and outline some challenges that remain to be addressed.

References in corpus (15)

• Biological network comparison using graphlet degree distribution
• Interaction Networks for Learning about Objects, Relations and Physics
• A Survey on Graph Kernels
• Subgraph Matching Kernels for Attributed Graphs
• Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels
• A Fair Comparison of Graph Neural Networks for Graph Classification
• Graph Kernels: State-of-the-Art and Future Challenges
• Wasserstein Weisfeiler-Lehman Graph Kernels
• DDGK: Learning Graph Representations for Deep Divergence Graph Kernels
• The optimal assignment kernel is not positive definite
• Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings
• RetGK: Graph Kernels based on Return Probabilities of Random Walks
• Convolutional Kernel Networks for Graph-Structured Data
• Pre-training Graph Neural Networks with Kernels
• Contextual Weisfeiler-Lehman Graph Kernel For Malware Detection

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• Graph Kernels: State-of-the-Art and Future Challenges
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• Interpretable Neural Architecture Search via Bayesian Optimisation with Weisfeiler-Lehman Kernels
• Understanding Isomorphism Bias in Graph Data Sets
• Deep Graph Similarity Learning: A Survey
• System Architecture Optimization Strategies: Dealing with Expensive Hierarchical Problems
• A Survey of Latent Factor Models in Recommender Systems
• EvoNet: A Neural Network for Predicting the Evolution of Dynamic Graphs
• Sample efficient graph classification using binary Gaussian boson sampling
• Benchmark of the Full and Reduced Effective Resistance Kernel for Molecular Classification
• Provenance Graph Kernel
• Topological Graph Neural Networks
• Generating the Graph Gestalt: Kernel-Regularized Graph Representation Learning
• Computational Pipeline to probe NaV1.7 gain-of-functions variants in neuropathic painful syndromes