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1. A Web-Based System for Contagion Simulations on Networked Populations

   https://doi.org/10.1109/eScience55777.2022.00044  

   Ferdousi, Tanvir; Kishore, Aparna; Machi, Lucas; Machi, Dustin; Kuhlman, Chris J.; Ravi, S. S. (October 2022, IEEE International Conference on e-Science)

   Motivated by a wide range of applications, research on agent-based models of contagion propagation over networks has attracted a lot of attention in the literature. Many of the available software systems for simulating such agent-based models require users to download software, build the executable, and set up execution environments. Further, running the resulting executable may require access to high performance computing clusters. Our work describes an open access software system (NetSimS) that works under the “Modeling and Simulation as a Service” (MSaaS) paradigm. It enables users to run simulations by selecting models and parameter values, initial conditions, and networks through a web interface. The system supports a variety of models and networks with millions of nodes and edges. In addition to the simulator, the system includes components that enable users to choose initial conditions for simulations in a variety of ways, to analyze the data generated through simulations, and to produce plots from the data. We describe the components of NetSimS and carry out a performance evaluation of the system. We also discuss two case studies carried out on large networks using the system. NetSimS is a major component within net.science, a cyberinfrastructure for network science. 

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2. A Framework for Simulating Multiple Contagions Over Multiple Networks

   Kishore, Aparna; Machi, Lucas; Kuhlman, Chris J.; Machi, Dustin; Ravi, S.S. (January 2021, Complex Networks and Their Applications)

   null (Ed.)

   Many contagion processes evolving on populations do so simultaneously, interacting over time. Examples are co-evolution of human social processes and diseases, such as the uptake of mask wearing and disease spreading. Commensurately, multi-contagion agent-based simulations (ABSs) that represent populations as networks in order to capture interactions between pairs of nodes are becoming more popular. In this work, we present a new ABS system that simulates any number of contagions co-evolving on any number of networked populations. Individual (interacting) contagion models and individual networks are speci ed, and the system computes multi-contagion dynamics over time. This is a signi cant improvement over simulation frameworks that require union graphs to handle multiple networks, and/or additional code to orchestrate the computations of multiple contagions. We provide a formal model for the simulation system, an overview of the software, and case studies that illustrate applications of interacting contagions. 

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3. A Framework for Simulating Multiple Contagions Over Multiple Networks

   https://doi.org/10.1007/978-3-030-93413-2_21  

   Kishore, Aparna; Machi, Lucas; Kuhlman, Chris J; Machi, Dustin; Ravi, S S. (January 2021, Complex Networks and their Applications)

   Many contagion processes evolving on populations do so simultaneously, interacting over time. Examples are co-evolution of human social processes and diseases, such as the uptake of mask wearing and disease spreading. Commensurately, multi-contagion agent-based simulations (ABSs) that represent populations as networks in order to capture interactions between pairs of nodes are becoming more popular. In this work, we present a new ABS system that simulates any number of contagions co-evolving on any number of networked populations. Individual (interacting) contagion models and individual networks are specified, and the system computes multi-contagion dynamics over time. This is a significant improvement over simulation frameworks that require union graphs to handle multiple networks, and/or additional code to orchestrate the computations of multiple contagions. We provide a formal model for the simulation system, an overview of the software, and case studies that illustrate applications of interacting contagions. 

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4. Overcoming Challenges in Large-Core SI-POF-Based System-Level Modeling and Simulation

   https://doi.org/10.3390/photonics6030088  

   Richards, Dwight; Lopez, Alicia; Losada, M. Angeles; Mena, Pablo V.; Ghillino, Enrico; Mateo, Javier; Antoniades, Neo; Jiang, Xin (September 2019, Photonics)

   The application areas for plastic optical fibers such as in-building or aircraft networks usually have tight power budgets and require multiple passive components. In addition, advanced modulation formats are being considered for transmission over plastic optical fibers (POFs) to increase spectral efficiency. In this scenario, there is a clear need for a flexible and dynamic system-level simulation framework for POFs that includes models of light propagation in POFs and the components that are needed to evaluate the entire system performance. Until recently, commercial simulation software either was designed specifically for single-mode glass fibers or modeled individual guided modes in multimode fibers with considerable detail, which is not adequate for large-core POFs where there are millions of propagation modes, strong mode coupling and high variability. These are some of the many challenges involved in the modeling and simulation of POF-based systems. Here, we describe how we are addressing these challenges with models based on an intensity-vs-angle representation of the multimode signal rather than one that attempts to model all the modes in the fiber. Furthermore, we present model approaches for the individual components that comprise the POF-based system and how the models have been incorporated into system-level simulations, including the commercial software packages SimulinkTM and ModeSYSTM. 

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5. GraphTrans: A Software System for Network Conversions for Simulation, Structural Analysis, and Graph Operations

   Carscadden, Henry L; Machi, Lucas; Kuhlman, Chris J; Machi, Dustin; Ravi, S S. (January 2021, Winter Simulation Conference)

   Network representations of socio-physical systems are ubiquitous, examples being social (media) networks and infrastructure networks like power transmission andwater systems. The many software tools that analyze and visualize networks, and carry out simulations on them, require different graph formats. Consequently, it is important to develop software for converting graphs that are represented in a given source format into a required representation in a destination format. For network-based computations, graph conversion is a key capability that facilitates interoperability among software tools. This paper describes such a system called GraphTrans to convert graphs among different formats. This system is part of a new cyberinfrastructure for network science called net.science. We present the GraphTrans system design and implementation, results from a performance evaluation, and a case study to demonstrate its utility. 

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