Lecca Paola, Lecca Michela
Faculty of Engineering, Free University of Bozen-Bolzano, Bolzano, Italy.
Fondazione Bruno Kessler, Digital Industry Center, Technologies of Vision, Trento, Italy.
Front Artif Intell. 2023 Nov 16;6:1256352. doi: 10.3389/frai.2023.1256352. eCollection 2023.
Graphs are used as a model of complex relationships among data in biological science since the advent of systems biology in the early 2000. In particular, graph data analysis and graph data mining play an important role in biology interaction networks, where recent techniques of artificial intelligence, usually employed in other type of networks (e.g., social, citations, and trademark networks) aim to implement various data mining tasks including classification, clustering, recommendation, anomaly detection, and link prediction. The commitment and efforts of artificial intelligence research in network biology are motivated by the fact that machine learning techniques are often prohibitively computational demanding, low parallelizable, and ultimately inapplicable, since biological network of realistic size is a large system, which is characterised by a high density of interactions and often with a non-linear dynamics and a non-Euclidean latent geometry. Currently, graph embedding emerges as the new learning paradigm that shifts the tasks of building complex models for classification, clustering, and link prediction to learning an informative representation of the graph data in a vector space so that many graph mining and learning tasks can be more easily performed by employing efficient non-iterative traditional models (e.g., a linear support vector machine for the classification task). The great potential of graph embedding is the main reason of the flourishing of studies in this area and, in particular, the artificial intelligence learning techniques. In this mini review, we give a comprehensive summary of the main graph embedding algorithms in light of the recent burgeoning interest in geometric deep learning.
自21世纪初系统生物学出现以来，图形被用作生物科学中数据之间复杂关系的模型。特别是，图形数据分析和图形数据挖掘在生物相互作用网络中发挥着重要作用，在这些网络中，通常用于其他类型网络（如社交、引用和商标网络）的人工智能最新技术旨在实现各种数据挖掘任务，包括分类、聚类、推荐、异常检测和链接预测。人工智能在网络生物学研究中的投入和努力源于这样一个事实，即机器学习技术通常计算要求过高、并行性低且最终无法应用，因为实际规模的生物网络是一个大型系统，其特点是相互作用密度高，且通常具有非线性动力学和非欧几里得潜在几何结构。目前，图形嵌入作为一种新的学习范式出现，它将构建用于分类、聚类和链接预测的复杂模型的任务转变为在向量空间中学习图形数据的信息表示，以便通过使用高效的非迭代传统模型（如用于分类任务的线性支持向量机）更轻松地执行许多图形挖掘和学习任务。图形嵌入的巨大潜力是该领域研究蓬勃发展的主要原因，尤其是人工智能学习技术。在这篇小型综述中，鉴于最近对几何深度学习的兴趣迅速增长，我们对主要的图形嵌入算法进行了全面总结。
