In general, networks or graphs (mathematical way of representing a network) are used to capture relationships between entities or objects. We hope this review becomes a useful handbook for readers regardless of their scientific background and help non-experts in handling and interpreting networks more easily. Finally, we try to bring graph theory into a biomedical context by providing a thorough description about the different types of biomedical networks and the sources used for their construction. We further comment on the various layout and graph drawing techniques as well as on methods regarding network alignment and link predictions and we highlight the state-of-the-art tools for analyzing such networks. In addition, we discuss several topological features and network properties, as well as concepts such as graph clustering, clustering comparison, network alignment, motif detection, and edge prediction. To do this, we start with an introduction about graphs (in discrete mathematics) and their different types and we further describe the various data structures and file formats for storage and representation. The aim of this review is to tackle questions raised by today's increasing demands and aid researchers in understanding the graph theory behind the biomedical networks as well as concepts such as visualization, annotation, management, clustering, integration, etc. While most recent review articles focus on biomedical and biological networks and their applications ( McGillivray et al., 2018 Sonawane et al., 2019 Yue et al., 2019), in certain case studies, familiarity with the graph theory concepts behind these networks is often missing. We expect this review to reach a very broad spectrum of readers varying from experts to beginners while encouraging them to enhance the field further. Finally, we discuss a variety of algorithms and metrics for network analyses regarding graph drawing, clustering, visualization, link prediction, perturbation, and network alignment as well as the current state-of-the-art tools. We briefly mention the network patterns, motifs and models, and we further comment on the types of biological and biomedical networks along with their corresponding computer- and human-readable file formats. In addition, we describe several network properties and we highlight some of the widely used network topological features. In this article, we discuss the basic graph theory concepts and the various graph types, as well as the available data structures for storing and reading graphs. Networks are one of the most common ways to represent biological systems as complex sets of binary interactions or relations between different bioentities. 3Lawrence Berkeley National Laboratory, Department of Energy, Joint Genome Institute, Walnut Creek, CA, United States.2Department of Informatics and Telecommunications, University of Athens, Athens, Greece.1Institute for Fundamental Biomedical Research, BSRC вЂњAlexander FlemingвЂќ, Vari, Greece.Mikaela Koutrouli 1 вЂ , Evangelos Karatzas 1,2 вЂ , David Paez-Espino 3 and Georgios A.

Cytoscape multiple edges