Computational Methods for Communication Networks

Communication networks shape information flow within societies, especially in the digital age, where social media and online platforms amplify their reach and complexity. This paper examines the use of computational methods, particularly Social Network Analysis (SNA), to study communication networks. Rooted in graph theory, SNA enables the analysis of various network structures, including monopartite, bipartite, multiplex, and multilayer networks. The paper reviews key computational tools and techniques, focusing on R and Python libraries, and explores their applications in understanding communication networks. It underscores the increasing role of computational methods in analyzing digital communication networks and their broader social effects, such as polarization, misinformation spread, and political activism.