Initializing Agent-Based Models with Clustering Archetypes

Saadat, Samaneh; Gunaratne, Chathika; Baral, Nisha; Sukthankar, Gita; Garibay, Iván

doi:10.1007/978-3-319-93372-6_27

Cited by 12 publications

(4 citation statements)

References 4 publications

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“…The other types of decompositional prediction methods utilize clustering. An initial model predicts the main user clusters, then a 2nd model predicts the users in each cluster [13], [14].…”

Section: Decompositional User-level Prediction In Social Mediamentioning

confidence: 99%

Simulating User-Level Twitter Activity with XGBoost and Probabilistic Hybrid Models

Mubang¹,

Hall²

2022

Preprint

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The Volume-Audience-Match simulator, or VAM was applied to predict future activity on Twitter related to international economic affairs. VAM was applied to do timeseries forecasting to predict the: (1) number of total activities, (2) number of active old users, and (3) number of newly active users over the span of 24 hours from the start time of prediction. VAM then used these volume predictions to perform user link predictions. A user-user edge was assigned to each of the activities in the 24 future timesteps. VAM considerably outperformed a set of baseline models in both the time series and user-assignment tasks.

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“…The other types of decompositional prediction methods utilize clustering. An initial model predicts the main user clusters, then a 2nd model predicts the users in each cluster [13], [14].…”

Section: Decompositional User-level Prediction In Social Mediamentioning

confidence: 99%

Simulating User-Level Twitter Activity with XGBoost and Probabilistic Hybrid Models

Mubang¹,

Hall²

2022

Preprint

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“…The parameters (e.g., speed of the predator, density, and detection distance of prey) are important as model results are sensitive to these conditions (Saadat et al, 2018). There are techniques available to choose these initial parameters such as using observations from field sites, existing datasets, or publications.…”

Section: What Is An Agent‐based Model?mentioning

confidence: 99%

An introduction to agent‐based models as an accessible surrogate to field‐based research and teaching

Murphy

Ciuti

Kane

2020

Ecology and Evolution

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Fieldwork encompasses any practical work taking place outside the laboratory for data collection and learning (Lock, 1998). Field data collection is essential to investigate long-term ecological processes and observe new phenomena for the first time. Moreover, university fieldwork is a central component of coursework as fieldwork-based skills including project design, surveying, data curation, and risk assessment are vital for students seeking work in a competitive job market (Pool & Sewell, 2007). Thus, fieldwork for research and teaching purposes provides essential training and experience for early-career researchers (Peacock & Bacon, 2018).

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“…The SHD model [12,20] is a replay-based data mixture model designed based on the seasonality characteristic of the OSN user activities and the hypothesis that the users exhibit repetitive patterns. This model extracts the most recent activities from the training data to provide the information related to the user interactions and edge formations in the network, and predicts the future user interactions according to the same types of activities in the past.…”

Section: Sampled Historical Datamentioning

confidence: 99%

Deep Agent: Studying the Dynamics of Information Spread and Evolution in Social Networks

Garibay,

Oghaz,

Yousefi

et al. 2020

Preprint

Self Cite

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This paper explains the design of a social network analysis framework, developed under DARPA's SocialSim program, with novel architecture that models human emotional, cognitive and social factors. Our framework is both theory and data-driven, and utilizes domain expertise. Our simulation effort helps understanding how information flows and evolves in social media platforms. We focused on modeling three information domains: cryptocurrencies, cyber threats, and software vulnerabilities for the three interrelated social environments: GitHub, Reddit, and Twitter. We participated in the SocialSim DARPA Challenge in December 2018, in which our models were subjected to an extensive performance evaluation for accuracy, generalizability, explainability, and experimental power. This paper reports the main concepts and models, utilized in our social media modeling effort in developing a multi-resolution simulation at the user, community, population, and content levels.

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Initializing Agent-Based Models with Clustering Archetypes

Cited by 12 publications

References 4 publications

Simulating User-Level Twitter Activity with XGBoost and Probabilistic Hybrid Models

Simulating User-Level Twitter Activity with XGBoost and Probabilistic Hybrid Models

An introduction to agent‐based models as an accessible surrogate to field‐based research and teaching

Deep Agent: Studying the Dynamics of Information Spread and Evolution in Social Networks

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