Generation and analysis of large synthetic social contact networks

Barrett, Jessica R.; Beckman,; Khan, M. R.; Kumar, Anita B.; Marathe,; Stretz,; Dutta,; Lewis, Elfed

doi:10.1109/wsc.2009.5429425

Cited by 144 publications

(132 citation statements)

References 15 publications

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“…Its input is a bipartite graph consisting of person and location nodes, with edges between them representing a visit by a person to a specific location at a specific time. This graph is a synthetic network based on census and other data [5]. We call this the person-location graph.…”

Section: A Contagion Simulation Structurementioning

confidence: 99%

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Overcoming the Scalability Challenges of Epidemic Simulations on Blue Waters

Yeom

Bhatele

Bisset

et al. 2014

2014 IEEE 28th International Parallel and Distributed Processing Symposium

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Section: A Contagion Simulation Structurementioning

confidence: 99%

“…The input graphs to EPISIMDEMICS, which are derived from real-world data [5], typically follow heavy-tailed degree distributions. This is common for social network graphs [7].…”

Section: Scalability Challenges With Social Network Datamentioning

confidence: 99%

Overcoming the Scalability Challenges of Epidemic Simulations on Blue Waters

Yeom

Bhatele

Bisset

et al. 2014

2014 IEEE 28th International Parallel and Distributed Processing Symposium

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“…Given the daily schedule of activities, agents are assigned locations for each of those activities. This may be done using gravity models in which the probability a location is selected for an activity is inversely proportional to its distance from anchor location(s), such as home or work [28] or trip chaining [29,30]. This step may be combined with the assignment of activity schedules, if the schedules are created dynamically.…”

Section: Introductionmentioning

confidence: 99%

A Two-stage, Fitted Values Approach to Activity Matching

Lum¹,

Chungbaek²,

Eubank³

et al. 2016

IJT

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“…A synthetic population of 19,000 college undergraduates. A social network of the state of Virginia was generated using the procedure in 25 and used as the starting point for our work. The social network contains synthetic individuals whose traits match in distribution the attributes of the actual population.…”

Section: Contributionsmentioning

confidence: 99%

“…We model the undergraduate student body of a large university. A modeling process 25 was used to construct this population, which creates anonymous students and endows them with traits such as age, gender, and sets of activities that result in daily face-to-face interactions with other students. The result of this process is a college social network, where nodes/agents represent students and undirected edges represent interactions between students.…”

Section: College Social Networkmentioning

confidence: 99%

Agent-Based Modeling and Simulation of Depression and Its Impact on Student Success and Academic Retention

Abdelhamid¹,

Kuhlman²,

Marathe³

et al.

2016 ASEE Annual Conference &Amp; Exposition Proceedings

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, he is interested in designing and building software systems to enable domain experts to easily access and effectively use high performance computing to perform and share the findings of simulations and large scale data analyses. Other aspects of his research focus on how to use these systems as learning tools for students and teachers. Dr. Chris J. Kuhlman, Virginia Bioinformatics InstituteChris is a Research Scientist at the Biocomplexity Institute at Virginia Tech. His research interests include discrete dynamical systems, agent-based modeling and simulation, distributed and high performance computing, algorithms, social sciences and modeling, and network science. In the U.S., major depressive disorder affects approximately 14.8 million American adults. Furthermore, depression can lead to a several other illnesses and disabilities. Economic burden of depression is estimated to be $53 billion annually in the U.S. alone. Depression can reach high levels that can lead to suicide, the third leading cause of death among the U.S. college-aged population.Studies show a direct relation between mental health and academic success. In particular, depression is a significant predictor of lower GPA and increased drop out rate. A 15 point increase on the depression scale correlates with a 0.17 drop in GPA and corresponds to a 4.7 percent increase in probability of dropping out. High dropout rates also adversely impact both universities and society.In this work, we construct and exercise an agent-based model (ABM) of the evolution of depression among a population of roughly 19,000 college students. This model includes within-agent interactions among depression symptoms and agent-to-agent interactions defined by a college student social network. We conduct simulation studies to identify (model) parameters and initial conditions that most influence population outcomes. Connectivity among within-agent symptoms is demonstrated to have a large effect on population levels of depression.

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Generation and analysis of large synthetic social contact networks

Cited by 144 publications

References 15 publications

Overcoming the Scalability Challenges of Epidemic Simulations on Blue Waters

Overcoming the Scalability Challenges of Epidemic Simulations on Blue Waters

A Two-stage, Fitted Values Approach to Activity Matching

Agent-Based Modeling and Simulation of Depression and Its Impact on Student Success and Academic Retention

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