The adaptive processing of structured data is a long-standing research topic in machine learning that investigates how to automatically learn a mapping from a structured input to outputs of various nature. Recently, there has been an increasing interest in the adaptive processing of graphs, which led to the development of different neural network-based methodologies. In this thesis, we take a different route and develop a Bayesian Deep Learning framework for graph learning. The dissertation begins with a review of the principles over which most of the methods in the field are built, followed by a study on graph classification reproducibility issues. We then proceed to bridge the basic ideas of deep learning for graphs with the Bayesian world, by building our deep architectures in an incremental fashion. This framework allows us to consider graphs with discrete and continuous edge features, producing unsupervised embeddings rich enough to reach the state of the art on several classification tasks. Our approach is also amenable to a Bayesian nonparametric extension that automatizes the choice of almost all model's hyper-parameters. Two real-world applications demonstrate the efficacy of deep learning for graphs. The first concerns the prediction of information-theoretic quantities for molecular simulations with supervised neural models. After that, we exploit our Bayesian models to solve a malware-classification task while being robust to intra-procedural code obfuscation techniques. We conclude the dissertation with an attempt to blend the best of the neural and Bayesian worlds together. The resulting hybrid model is able to predict multimodal distributions conditioned on input graphs, with the consequent ability to model stochasticity and uncertainty better than most works. Overall, we aim to provide a Bayesian perspective into the articulated research field of deep learning for graphs.
翻译：结构化数据的适应处理是机器学习中一个长期的研究课题, 研究如何自动从结构化输入到各种自然产出中进行绘图。 最近, 对图表的适应性处理越来越感兴趣, 这导致不同神经网络方法的发展。 在这个论文中, 我们选择了不同的路线, 并开发了一个用于图形学习的Bayesian深层学习框架。 论文首先审查了在实地建立大多数方法的原则, 之后又研究了图形级的碱性分类再生问题。 我们接着通过逐步建立我们的深层结构, 来将深层的图表的深度精度学习理念与巴伊西亚世界联系起来。 这个框架让我们能够考虑具有离散和连续的边缘特征的图表, 产生足够丰富的、 足以达到图表学习状态的不优于贝伊斯深层深层深层深层学习的模型。 我们的方法也可以是拜伊斯模型的不直径直径直径直径延伸的模型, 使得几乎所有模型的直位值都自动化。 我们的两个实体应用程序展示了我们深层次的混合的数学预估能力, 和最深层的模型在模拟中进行模拟的模型的模型后, 我们的测算的模型的模型, 我们的预判, 我们的模型的模型的预测测测测, 我们的测测测测了世界的模型, 我们的模型的模型, 我们的模型的测, 我们的测测测测测测测测测测测到了。