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Information thermodynamics in stochastic Boolean networks
(University of Tokyo)
Complex networks have attracted much attention in various fields, and a variety of analysis methods have been adopted. One approach is to focus on small subgraph patterns that can be seen as building blocks of a large-scale network. While many studies have investigated the functional roles of such patterns, their thermodynamic aspects have not been much explored. In a different context, information thermodynamics has been developed to quantify dissipation of fluctuating subsystems where information flow plays a crucial role. Here, we consider a simple model called stochastic Boolean network, and propose a systematic characterization of subgraph patterns on the basis of information thermodynamics. Specifically, we focus on three-node patterns, which receive one or two input signals that carry external information. For the case of a single input, we found that all the three-node patterns are classified into four types by using information-thermodynamic measures such as dissipation and mutual information. Next, we consider the case that there are two input, and evaluate the capacity of logical operation of the three-node patterns by using tripartite mutual information, and found that patterns with fewer edges make logical operation efficient.