Abstract
Graph theory is a branch of mathematics that models data as networks, where a node represents a data element and an edge between nodes signifies a relationship between elements. Due to the properties of graph theory, it can be particularly useful for modeling and representing complex biological systems. Each biological phenomenon can indeed be represented as a network, and graph theory offers effective mathematical and computational tools to analyze and interpret this type of data. However, applying network science to biology can be tricky and, if done poorly, may lead to common mistakes and problems in scientific analysis. These issues, in turn, can result in misleading conclusions and wrong outcomes. To avoid these common pitfalls, we present nine quick recommendations for using graph theory to analyze the interactions among the components of biological systems in an accurate and efficient way. While we originally designed our guidelines for beginners, we believe they should also be considered by experts.