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Title Large-scale Agent-based Simulations of Online Social Networks
Authors Goran Murić, Alexey Tregubov, Jim Blythe, Andrés Abeliuk, Divya Choudhary, Kristina Lerman, Emilio Ferrara
Publication date June 2022
Abstract As part of the DARPA SocialSim challenge, we address the
of predicting behavioral phenomena including information spread involving
hundreds of thousands of users across three major linked social networks:
Twitter, Reddit and GitHub. Our approach develops a framework for
data-driven agent simulation that begins with a discrete-event simulation of
the environment populated with generic, flexible agents, then optimizes the
decision model of the agents by combining a number of machine learning
classification problems. The ML problems predict when an agent will take a
certain action in its world and are designed to combine aspects of the
agents, gathered from historical data, with dynamic aspects of the
environment including the resources, such as tweets, that agents interact
with at a given point in time. In this way, each of the agents makes
individualized decisions based on their environment, neighbors and history
during the simulation, although global simulation data is used to learn
accurate generalizations. This approach showed the best performance of all
participants in the DARPA challenge across a broad range of metrics. We
describe the performance of models both with and without machine learning on
measures of cross-platform information spread defined both at the level of
the whole population and at the community level. The best performing model
overall combines learned agent behaviors with explicit modeling of bursts in
global activity. Because of the general nature of our approach, it is
applicable to a range of prediction problems that require modeling
individualized, situational agent behavior from trace data that combines
many agents.
Volume 36
Journal name Autonomous Agents and Multi-Agent Systems
Publisher Springer (Netherlands)
Reference URL View reference page