Abstract
Graphs are used as 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, 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, ultimately inapplicable, since biological network of realistic size are large systems, 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 learn 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, in the artificial intelligence learning techniques. In this mini review we give a comprehensive summary of the main graph embedding algorithms in the light of the recent burgeoning interest in geometric deep learning.