text: 286,104,1415475602832781315,901142962758758400,Hang-Hyun Jo,"Our new paper with Yohsuke Murase @yohm13, Janos Török, Janos Kertesz @janos_kertesz, and Kimmo Kaski @kimmokaski is out in the arXiv: ""Deep learning based parameter search for an agent based social network model"", see <LINK>. Since 2014 we have studied the social network modeling based on Kumpula et al.'s model (<LINK>). Our new paper is the synthesis of (almost) all of our previous works: <LINK>, <LINK>, and <LINK>. I omitted the figure in the paper. By the way, you can watch the simulation video for the original Kumpula et al.'s model <LINK> and its multilayer version (our work) <LINK>, both of which are made by @yohm13. <LINK>",https://arxiv.org/abs/2107.06507,"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. ","Deep learning based parameter search for an agent based social network