Abstract | ||
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Networks are a very convenient and tractable way to model and represent interactions among entities. For example, they are often used in agent-based models to describe agents' acquaintances. Yet, data on real-world networks are missing or difficult to gather. Being able to generate synthetic but realistic social networks is thus an important challenge in social simulation. In this article, we provide a very comprehensive and modular agent-based process of network creation. We believe that the complexity of ABM (Agent-Based Models) comes from the overall interactions of entities, but they could be kept very simple for better control over the outcome. The idea is to use an agent-based simulation to generate networks: agent behaviors are rules for the network construction. Because we want the process to be dynamic and resilient to nodes perturbation, we provide a way for behaviors to spread among agents, following the meme basic principle - spreading by imitation. Resulting generated networks are compared to a target network; the system automatically looks at the best behavior distribution to generate this specific target network. |
Year | DOI | Venue |
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2017 | 10.5220/0006202705990606 | ICAART: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE, VOL 2 |
Keywords | Field | DocType |
Synthetic Network Generation,Agent-based Modeling,Network Dynamic | Network generation,Social network,Computer science,Social simulation,Network construction,Imitation,Artificial intelligence,Modular design,Machine learning,Distributed computing | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Audren Bouadjio-Boulic | 1 | 2 | 1.05 |
Frédéric Amblard | 2 | 430 | 51.43 |
Benoit Gaudou | 3 | 212 | 29.08 |