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In group anagram games, players cooperate to form words by sharing letters that they are initially given. The aim is to form as many words as possible as a group, within five minutes. Players take several different actions: requesting letters from their neighbors, replying to letter requests, and forming words. Agentbased models (ABMs) for the game compute likelihoods of each player’s next action, which contain uncertainty, as they are estimated from experimental data. We adopt a Bayesian approach as a natural means of quantifying uncertainty, to enhance the ABM for the group anagram game. Specifically, a Bayesian nonparametric clustering method is used to group player behaviors into different clusters without prespecifying the number of clusters. Bayesian multi nominal regression is adopted to model the transition probabilities among different actions of the players in the ABM. We describe the methodology and the benefits of it, and perform agentbased simulations of the game.Free, publiclyaccessible full text available December 11, 2023

Heterogeneous player behaviors are commonly observed in games. It is important to quantify and visualize these heterogeneities in order to understand collective behaviors. Our work focuses on developing a Bayesian approach for uncertainty visualization in a model of networked anagram games. In these games, team members collectively form as many words as possible by sharing letters with their neighbors in a network. Heterogeneous player behaviors include great differences in numbers of words formed and the amount of cooperation among networked neighbors. Our Bayesian approach provides meaningful insights for inferring worst, average, and best player performance within behavioral clusters, overcoming previous model shortcomings. These inferences are integrated into a simulation framework to understand the implications of model uncertainty and players' heterogeneous behaviors.Free, publiclyaccessible full text available November 8, 2023

There are myriad reallife examples of contagion processes on human social networks, e.g., spread of viruses, information, and social unrest. Also, there are many methods to control or block contagion spread. In this work, we introduce a novel method of blocking contagions that uses nodes from dominating sets (DSs). To our knowledge, this is the first use of DS nodes to block contagions. Finding minimum dominating sets of graphs is an NPComplete problem, so we generalize a wellknown heuristic, enabling us to customize its execution. Our method produces a prioritized list of dominating nodes, which is, in turn, a prioritized list of blocking nodes. Thus, for a given network, we compute this list of blocking nodes and we use it to block contagions for all blocking node budgets, contagion seed sets, and parameter values of the contagion model. We report on computational experiments of the blocking efficacy of our approach using two mined networks. We also demonstrate the effectiveness of our approach by comparing blocking results with those from the high degree heuristic, which is a common standard in blocking studies.Free, publiclyaccessible full text available November 10, 2023

Motivated by a wide range of applications, research on agentbased models of contagion propagation over networks has attracted a lot of attention in the literature. Many of the available software systems for simulating such agentbased 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.Free, publiclyaccessible full text available October 10, 2023

Abstract We consider the simultaneous propagation of two contagions over a social network. We assume a threshold model for the propagation of the two contagions and use the formal framework of discrete dynamical systems. In particular, we study an optimization problem where the goal is to minimize the total number of new infections subject to a budget constraint on the total number of available vaccinations for the contagions. While this problem has been considered in the literature for a single contagion, our work considers the simultaneous propagation of two contagions. This optimization problem is NPhard. We present two main solution approaches for the problem, namely an integer linear programming (ILP) formulation to obtain optimal solutions and a heuristic based on a generalization of the set cover problem. We carry out a comprehensive experimental evaluation of our solution approaches using many realworld networks. The experimental results show that our heuristic algorithm produces solutions that are close to the optimal solution and runs several orders of magnitude faster than the ILPbased approach for obtaining optimal solutions. We also carry out sensitivity studies of our heuristic algorithm.

We consider the simultaneous propagation of two contagions over a social network. We assume a threshold model for the propagation of the two contagions and use the formal framework of discrete dynamical systems. In particular, we study an optimization problem where the goal is to minimize the total number of new infections subject to a budget constraint on the total number of available vaccinations for the contagions. While this problem has been considered in the literature for a single contagion, our work considers the simultaneous propagation of two contagions. This optimization problem is NPhard. We present two main solution approaches for the problem, namely an integer linear programming (ILP) formulation to obtain optimal solutions and a heuristic based on a generalization of the set cover problem. We carry out a comprehensive experimental evaluation of our solution approaches using many realworld networks. The experimental results show that our heuristic algorithm produces solutions that are close to the optimal solution and runs several orders of magnitude faster than the ILPbased approach for obtaining optimal solutions. We also carry out sensitivity studies of our heuristic algorithm.

Neighborhood effects have an important role in evacuation decisionmaking 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 thresholdbased graph dynamical system model, 2modethreshold, which captures this dichotomy. We study theoretically the dynamical properties of 2modethreshold in different networks, and find significant differences 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 agentbased simulations on these small world networks. We use it to understand evacuation rates in this region, and to evaluate the effects of modeling parameters on evacuation decision dynamics. Specifically, we quantify the effects of (1) network generation parameters, (2) stochasticity in the social network generation process, (3) model types (2modethreshold vs. standard threshold models), (4) 2modethreshold modelmore »

Networkrepresentationsofsociophysicalsystemsareubiquitous,examplesbeingsocial(media)networks and infrastructurenetworkslikepowertransmissionandwatersystems.Themanysoftwaretoolsthatanalyze and visualizenetworks,andcarryoutsimulationsonthem,requiredifferentgraphformats.Consequently, it isimportanttodevelopsoftwareforconvertinggraphsthatarerepresentedinagivensourceformatintoa required representationinadestinationformat.Fornetworkbasedcomputations,graphconversionisakey capability thatfacilitatesinteroperabilityamongsoftwaretools.Thispaperdescribessuchasystemcalled GraphTrans to convertgraphsamongdifferentformats.Thissystemispartofanewcyberinfrastructure for networksciencecalled net.science. Wepresentthe GraphTrans system designandimplementation, results fromaperformanceevaluation,andacasestudytodemonstrateitsutility.

We study evacuation dynamics in a major urban region (Miami, FL) using a combination of a realistic population and social contact network, and an agentbased model of evacuation behavior that takes into account peer influence and concerns of looting. These factors have been shown to be important in prior work, and have been modeled as a thresholdbased network dynamical systems model (2modethreshold), which involves two threshold parametersfor a family's decision to evacuate and to remain in place for looting and crime concernsbased on the fraction of neighbors who have evacuated. The dynamics of such models are not well understood, and we observe that the threshold parameters have a significant impact on the evacuation dynamics. We also observe counterintuitive effects of increasing the evacuation threshold on the evacuated fraction in some regimes of the model parameter space, which suggests that the details of realistic networks matter in designing policies.

Data from surveys administered after Hurricane Sandy provide a wealth of information that can be used to develop models of evacuation decisionmaking. We use a model based on survey data for predicting whether or not a family will evacuate. The model uses 26 features for each household including its neighborhood characteristics. We augment a 1.7 million node householdlevel synthetic social network of Miami, Florida with public data for the requisite model features so that our population is consistent with the surveybased model. Results show that household features that drive hurricane evacuations dominate the effects of specifying large numbers of families as \early evacuators" in a contagion process, and also dominate effects of peer influence to evacuate. There is a strong networkbased evacuation suppression effect from the fear of looting. We also study spatial factors affecting evacuation rates as well as policy interventions to encourage evacuation.