Deep learning based parameter search for an agent based social network model
arXiv:2107.06507 · doi:10.3389/fdata.2021.739081
Abstract
Interactions between humans give rise to complex social networks that are characterized by heterogeneous degree distribution, weight-topology relation, overlapping community structure, and dynamics of links. Understanding such networks is a primary goal of science due to serving as the scaffold for many emergent social phenomena from disease spreading to political movements. An appropriate tool for studying them is agent-based modeling, in which nodes, representing persons, make decisions about creating and deleting links, thus yielding various macroscopic behavioral patterns. Here we focus on studying a generalization of the weighted social network model, being one of the most fundamental agent-based models for describing the formation of social ties and social networks. This Generalized Weighted Social Network (GWSN) model incorporates triadic closure, homophilic interactions, and various link termination mechanisms, which have been studied separately in the previous works. Accordingly, the GWSN model has an increased number of input parameters and the model behavior gets excessively complex, making it challenging to clarify the model behavior. We have executed massive simulations with a supercomputer and using the results as the training data for deep neural networks to conduct regression analysis for predicting the properties of the generated networks from the input parameters. The obtained regression model was also used for global sensitivity analysis to identify which parameters are influential or insignificant. We believe that this methodology is applicable for a large class of complex network models, thus opening the way for more realistic quantitative agent-based modeling.
12 pages, 4 figures, 3 tables, 1 pseudocode
References in corpus (27)
- Scikit-learn: Machine Learning in Python
- Epidemic processes in complex networks
- Multilayer Networks
- The structure and dynamics of multilayer networks
- Temporal Networks
- Spatial Networks
- Structure and tie strengths in mobile communication networks
- Networks beyond pairwise interactions: structure and dynamics
- Random walks and diffusion on networks
- Homophily, Cultural Drift and the Co-Evolution of Cultural Groups
- Analysis of a large-scale weighted network of one-to-one human communication
- Emergence of communities in weighted networks
- Bursty Human Dynamics
- Structural transition in social networks: The role of homophily
- Time scale competition leading to fragmentation and recombination transitions in the coevolution of network and states
- Emergence of Bursts and Communities in Evolving Weighted Networks
- Multilayer weighted social network model
- Divergent Time Scale in Axelrod Model Dynamics
- Fragmentation transitions in a coevolving nonlinear voter model
- Modeling the role of relationship fading and breakup in social network formation
- A tool for parameter-space explorations
- Cluster size entropy in the Axelrod model of social influence: small-world networks and mass media
- What does Big Data tell? Sampling the social network by communication channels
- An open-source job management framework for parameter-space exploration: OACIS
- Stylized facts in social networks: Community-based static modeling
- Diffusion of innovations in Axelrod's model
- CARAVAN: a framework for comprehensive simulations on massive parallel machines