Network structure optimization is a fundamental task in complex network
analysis. However, almost all the research on Bayesian optimization is aimed at
optimizing the objective functions with vectorial inputs. In this work, we
first present a flexible framework, denoted graph Bayesian optimization, to
handle arbitrary graphs in the Bayesian optimization community. By combining
the proposed framework with graph kernels, it can take full advantage of
implicit graph structural features to supplement explicit features guessed
according to the experience, such as tags of nodes and any attributes of
graphs. The proposed framework can identify which features are more important
during the optimization process. We apply the framework to solve four problems
including two evaluations and two applications to demonstrate its efficacy and
potential applications.

本文提出了一种灵活的图贝叶斯优化框架，结合了图内核，通过识别优化过程中哪些特征更重要来完全利用隐含的图结构特征并证明了其有效性和潜在应用。

图形贝叶斯优化：算法、评估和应用

Graph Bayesian Optimization: Algorithms, Evaluations and Applications

Deep neural networks (DNNs) are powerful machine learning models and have
succeeded in various artificial intelligence tasks. Although various
architectures and modules for the DNNs have been proposed, selecting and
designing the appropriate network structure for a target problem is a
challenging task. In this paper, we propose a method to simultaneously optimize
the network structure and weight parameters during neural network training. We
consider a probability distribution that generates network structures, and
optimize the parameters of the distribution instead of directly optimizing the
network structure. The proposed method can apply to the various network
structure optimization problems under the same framework. We apply the proposed
method to several structure optimization problems such as selection of layers,
selection of unit types, and selection of connections using the MNIST,
CIFAR-10, and CIFAR-100 datasets. The experimental results show that the
proposed method can find the appropriate and competitive network structures.

本文提出一种方法在深度神经网络训练期间同时优化网络结构和权重参数的概率分布，可用于解决多种网络结构优化问题，包括层次选择、单元类型选择和连接选择，实验结果表明该方法能够找到适当且具有竞争力的网络结构。