The ever-increasing importance of structured data in different applications, especially in the biomedical field, has driven the need for reducing its complexity through projections into a more manageable space. The latest methods for learning features on graphs focus mainly on the neighborhood of nodes and edges. Methods capable of providing a representation that looks beyond the single node neighborhood are kernel graphs. However, they produce handcrafted features unaccustomed with a generalized model. To reduce this gap, in this work we propose a neural embedding framework, based on probability distribution representations of graphs, named Netpro2vec. The goal is to look at basic node descriptions other than the degree, such as those induced by the Transition Matrix and Node Distance Distribution. Netpro2vec provides embeddings completely independent from the task and nature of the data. The framework is evaluated on synthetic and various real biomedical network datasets through a comprehensive experimental classification phase and is compared to well-known competitors.

中文翻译：

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Netpro2vec：生物医学应用的图嵌入框架


结构化数据在不同应用中的重要性不断增加，特别是在生物医学领域，这推动了通过预测到更易于管理的空间来降低其复杂性的需求。学习图特征的最新方法主要关注节点和边的邻域。能够提供超越单节点邻域的表示的方法是核图。然而，他们产生了不适应通用模型的手工特征。为了缩小这一差距，在这项工作中，我们提出了一种基于图的概率分布表示的神经嵌入框架，名为 Netpro2vec。目标是查看除度数之外的基本节点描述，例如由转移矩阵和节点距离分布引起的描述。 Netpro2vec 提供完全独立于任务和数据性质的嵌入。该框架通过综合实验分类阶段在合成和各种真实生物医学网络数据集上进行评估，并与知名竞争对手进行比较。