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.
more »
« less
A Web-based System for Contagion Simulations on Networked Populations
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 allows users to run simulations by
selecting agent-based models and parameters, 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 allow 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.
Index Terms—Agent-Based Simulation, Contagion, Networks,
Modeling and Simulation as a Service, Cyberinfrastructure
more »
« less
- NSF-PAR ID:
- 10376917
- Date Published:
- Journal Name:
- IEEE eScience
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Geospatial research and education have become increasingly dependent on cyberGIS to tackle computation and data challenges. However, the use of advanced cyberinfrastructure resources for geospatial research and education is extremely challenging due to both high learning curve for users and high software development and integration costs for developers, due to limited availability of middleware tools available to make such resources easily accessible. This tutorial describes CyberGIS-Compute as a middleware framework that addresses these challenges and provides access to high-performance resources through simple easy to use interfaces. The CyberGIS-Compute framework provides an easy to use application interface and a Python SDK to provide access to CyberGIS capabilities, allowing geospatial applications to easily scale and employ advanced cyberinfrastructure resources. In this tutorial, we will first start with the basics of CyberGISJupyter and CyberGIS-Compute, then introduce the Python SDK for CyberGIS-Compute with a simple Hello World example. Then, we will take multiple real-world geospatial applications use-cases like spatial accessibility and wildfire evacuation simulation using agent based modeling. We will also provide pointers on how to contribute applications to the CyberGIS-Compute framework.more » « less
-
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.more » « less
-
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.more » « less
-
null (Ed.)Neighborhood e ects have an important role in evacuation decision-making by a family. Owing to peer influence, neighbors evacuating can motivate a family to evacuate. Paradoxically, if a lot of neighbors evacuate, then the likelihood of an individual or family deciding to evacuate decreases, for fear of crime and looting. Such behavior cannot be captured using standard models of contagion spread on networks, e.g., threshold, independent cascade, and linear threshold models. Here, we propose a new threshold-based graph dynamical system model, 2mode-threshold, which captures this dichotomy. We study theoretically the dynamical properties of 2mode-threshold in di fferent networks, and fi nd signi ficant diff erences from a standard threshold model. We build and characterize small world networks of Virginia Beach, VA, where nodes are geolocated families (households) in the city and edges are interactions between pairs of families. We demonstrate the utility of our behavioral model through agent-based simulations on these small world networks. We use it to understand evacuation rates in this region, and to evaluate the e ffects of modeling parameters on evacuation decision dynamics. Speci fically, we quantify the effects of (i) network generation parameters, (ii) stochasticity in the social network generation process, (iii) model types (2mode-threshold vs. stan- dard threshold models), (iv) 2mode-threshold model parameters, (v) and initial conditions, on computed evacuation rates and their variability. An illustrative example result shows that the absence of looting e ect can overpredict evacuation rates by as much as 50%.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
