This leads to the desire of an accurate space encoding that enables performance prediction In this paper, we propose an approach to neural architecture via surrogates and black-box optimization to find search (NAS) based on graph embeddings. NAS has high-performing architectures in a continuous search space been addressed previously using discrete, sampling based [67]. Zhang et al. [67] propose D-VAE, a graph neural network methods, which are computationally expensive as well as (GNN) [14, 23, 56] based variational neural architecture differentiable approaches, which come at lower costs but embedding with emphasis on the information flow and enforce stronger constraints on the search space. The proposed thereby achieve good results in architecture performance approach leverages advantages from both sides by prediction and BO on the ENAS search space [39] and on a building a smooth variational neural architecture embedding dataset of Bayesian Networks.

In recent years there has been a rapid increase in classification methods on graph structured data. Both in graph kernels and graph neural networks, one of the implicit assumptions of successful state-of-the-art models was that incorporating graph isomorphism features into the architecture leads to better empirical performance. However, as we discover in this work, commonly used data sets for graph classification have repeating instances which cause the problem of isomorphism bias, i.e. artificially increasing the accuracy of the models by memorizing target information from the training set. This prevents fair competition of the algorithms and raises a question of the validity of the obtained results. We analyze 54 data sets, previously extensively used for graph-related tasks, on the existence of isomorphism bias, give a set of recommendations to machine learning practitioners to properly set up their models, and open source new data sets for the future experiments.