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Abstract
Promising steps have been taken to shift the research focus from individual molecules to groups of molecules. Thanks to advances in high-throughput technologies, large-scale biological datasets have become available at an unprecedented rate. As a result, a systematic approach is needed to reveal design principles of biological processes. This is achievable through modeling molecular activities as a network. Researchers have focused on the structural and statistical properties of such networks to investigate features of biological processes. In this transition, fundamental concepts in graph theory help researchers in the analysis of biological networks. From the viewpoint of graph theory, network construction methods in conjunction with popular visualization techniques are discussed. We also study the modularity properties of biological networks using complex clustering and community detection algorithms. This article aims to provide a comprehensive review about numerous applications of graph theory concepts in analyzing biological networks from different perspectives.