A Tree-Based Kernel for Graphs

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How researchers see this paper

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Ordered Decompositional DAG kernels enhancements (2015, methodology): “In this section, we first recall the ODDSTh kernel, presenting it in a slightly different way than as it was originally introduced in the paper [27].”
On Filter Size in Graph Convolutional Networks (2018, background): “State-of-the-art machine learning techniques for classiﬁcation and regression on graphs are at the moment kernel machines equipped with speciﬁcally designed kernels for graphs (e.g, [1]–[3]).”
A survey on graph kernels (2019, methodology): “In (Da San Martino et al. 2012a), a fast hashing-based computation scheme for the aforementioned graph kernel is proposed.”
A memory efficient graph kernel (2012, methodology): “Hinging on this observation, it may be possible to represent explicitly a very expressive feature space in a very compact way, simultaneously allowing fast computation of kernel values [9] (ODD kernels).”
Generative Kernels for Tree-Structured Data (2018, methodology): “The latter two benchmarks in Table III pertain to the classification of chemical compounds originally represented as more general classes of graphs and transformed into tree structures by means of the approach described in [51].”