2017/05/15 by Nicolás Rubido, Celso Grebogi, Rubido, Nicolás +3
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Physics and Astronomy · #Adaptation and Self-Organizing Systems (nlin.AO) #Complex Network Analysis Techniques #FOS: Computer and information sciences #FOS: Physical sciences #Gene Regulatory Network Analysis #Information Theory (cs.IT) #Opinion Dynamics and Social Influence #cs.IT #math.IT #nlin.AO
paper · pdf · doi:10.48550/arxiv.1705.05287
5 pages, 3 figures, proceedings paper
arxiv created 2017/05/15 · openalex publication_date 2017/05/15 · arxiv updated 2017/05/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Information Theory concepts and methodologies conform the background of how communication systems are studied and understood. They are mainly focused on the source-channel-receiver problem and on the asymptotic limits of accuracy and communication rates, which are the classical problems studied by Shannon. However, the impact of Information Theory on networks (acting as the channel) is just starting. Here, we present an approach to understand how information flows in any connected complex network. Our approach is based on defining linear conservative flows that travel through the network from source to receiver. This framework allows us to have an analytical description of the problem and also linking the topological invariants of the network, such as the node degree, with the information flow. In particular, our approach is able to deal with information transmission in modular networks (networks containing community structures) or multiplex networks (networks with multiple layers), which are nowadays of paramount importance.