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Graph Kernels: State-of-the-Art and Future Challenges
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Graph-structured data are an integral part of many application domains, including chemoinformatics, computational biology, neuroimaging, and social network analysis. Over the last two decades, numerous graph kernels, i.e. kernel functions between graphs, have been proposed to solve the problem of assessing the similarity between graphs, thereby making it possible to perform predictions in both classification and regression settings. This manuscript provides a review of existing graph kernels, their applications, software plus data resources, and an empirical comparison of state-of-the-art graph kernels.
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Scalable Machine Learning Algorithms using Path Signatures
Path signatures can be embedded in Gaussian process, deep learning, kernel, and graph diffusion models to match or beat established baselines on time series and graph benchmarks.
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