56,036 research outputs found
Analysis and evaluation of the entropy indices of a static network structure
Although degree distribution entropy (DDE), SD structure entropy (SDSE), Wu structure entropy (WSE) and FB structure entropy (FBSE) are four static network structure entropy indices widely used to quantify the heterogeneity of a complex network, previous studies have paid little attention to their differing abilities to describe network structure. We calculate these four structure entropies for four benchmark networks and compare the results by measuring the ability of each index to characterize network heterogeneity. We find that SDSE and FBSE more accurately characterize network heterogeneity than WSE and DDE. We also find that existing benchmark networks fail to distinguish SDSE and FBSE because they cannot discriminate local and global network heterogeneity. We solve this problem by proposing an evolving caveman network that reveals the differences between structure entropy indices by comparing the sensitivities during the network evolutionary process. Mathematical analysis and computational simulation both indicate that FBSE describes the global topology variation in the evolutionary process of a caveman network, and that the other three structure entropy indices reflect only local network heterogeneity. Our study offers an expansive view of the structural complexity of networks and expands our understanding of complex network behavior.The authors would like to thank the financial support of the National Natural Science Foundation of China (71501153), Natural Science Foundation of Shaanxi Province of China (2016JQ6072), and the Foundation of China Scholarship Council (201506965039, 201606965057). (71501153 - National Natural Science Foundation of China; 2016JQ6072 - Natural Science Foundation of Shaanxi Province of China; 201506965039 - Foundation of China Scholarship Council; 201606965057 - Foundation of China Scholarship Council)Published versio
Methodology for Simulation and Analysis of Complex Adaptive Supply Network Structure and Dynamics Using Information Theory
Supply networks existing today in many industries can behave as complex adaptive systems making them more difficult to analyze and assess. Being able to fully understand both the complex static and dynamic structures of a complex adaptive supply network (CASN) are key to being able to make more informed management decisions and prioritize resources and production throughout the network. Previous efforts to model and analyze CASN have been impeded by the complex, dynamic nature of the systems. However, drawing from other complex adaptive systems sciences, information theory provides a model-free methodology removing many of those barriers, especially concerning complex network structure and dynamics. With minimal information about the network nodes, transfer entropy can be used to reverse engineer the network structure while local transfer entropy can be used to analyze the network structure’s dynamics. Both simulated and real-world networks were analyzed using this methodology. Applying the methodology to CASNs allows the practitioner to capitalize on observations from the highly multidisciplinary field of information theory which provides insights into CASN’s self-organization, emergence, stability/instability, and distributed computation. This not only provides managers with a more thorough understanding of a system’s structure and dynamics for management purposes, but also opens up research opportunities into eventual strategies to monitor and manage emergence and adaption within the environment
Trajectories entropy in dynamical graphs with memory
In this paper we investigate the application of non-local graph entropy to
evolving and dynamical graphs. The measure is based upon the notion of Markov
diffusion on a graph, and relies on the entropy applied to trajectories
originating at a specific node. In particular, we study the model of
reinforcement-decay graph dynamics, which leads to scale free graphs. We find
that the node entropy characterizes the structure of the network in the two
parameter phase-space describing the dynamical evolution of the weighted graph.
We then apply an adapted version of the entropy measure to purely memristive
circuits. We provide evidence that meanwhile in the case of DC voltage the
entropy based on the forward probability is enough to characterize the graph
properties, in the case of AC voltage generators one needs to consider both
forward and backward based transition probabilities. We provide also evidence
that the entropy highlights the self-organizing properties of memristive
circuits, which re-organizes itself to satisfy the symmetries of the underlying
graph.Comment: 15 pages one column, 10 figures; new analysis and memristor models
added. Text improve
Hide and seek on complex networks
Signaling pathways and networks determine the ability to communicate in
systems ranging from living cells to human society. We investigate how the
network structure constrains communication in social-, man-made and biological
networks. We find that human networks of governance and collaboration are
predictable on teat-a-teat level, reflecting well defined pathways, but
globally inefficient. In contrast, the Internet tends to have better overall
communication abilities, more alternative pathways, and is therefore more
robust. Between these extremes the molecular network of Saccharomyces cerevisea
is more similar to the simpler social systems, whereas the pattern of
interactions in the more complex Drosophilia melanogaster, resembles the robust
Internet.Comment: 5 pages, 5 figure
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