Networks provide a simple model to understand and predict the emergent behavior of complex systems made of a large number of interacting nonlinear dynamical units. Some of the most challenging and useful problems in network science focus on how structural properties of the underlying interaction network govern the collective dynamical behavior on the network, and how these rules can be discovered from real-world data. The rapid advancement of new machine learning techniques has led to the development of new algorithms and strategies for inference of underlying network structures in datasets, prediction of behavior of complex and dynamical networks, and identification and control of such networks.
In this second installment of the satellite, we are covering topics like machine-learning-based network inference problems, analysis of social networking and human behavior data, and prediction of behavior of complex systems. Social data analysis has emerged as an important topic in the recent years because we have seen a rapid growth of online polarization, and also because new data has been available for human social interactions. Using machine learning for dynamical systems has become important in fields like global climate prediction, and relevant for accelerating state-of-the-art simulations of complex systems.
This satellite meeting aims to bring together the network scientists having expertise in traditional approaches and expert machine learning scientists for exchange of ideas, and formation of a platform for future collaboration, as well as to deliberate upon open problems in the network science which can be addressed by machine learning techniques.

More at: sites.google.com

This workshop is co-located with the Conference on Complex Systems and is interested in novel and innovative applications of complex adaptive system approaches to rising questions in the field of Management and Organization Science. The aim of the workshop is to facilitate the meeting of people who work in the field of Management and Organization Science (as well as adjacent areas) and who employ complex adaptive system approaches. We aim at providing a multidisciplinary forum for the presentation and discussion of recent research findings. The workshop has the ultimate objectives to bridge the gap between Management and Organization Science and Complexity Science, and to promote thereby the further development of Management and Organization Science with a specific emphasis to dynamics and emergent behaviors.

More at: casmos.github.io

Background. Cognition arises from multiple mental and biological processes which regulate the acquisition, perception and use of information. Understanding cognitive processes and their biological counterparts poses several, interconnected research challenges, attracting the attention of computational fields like cognitive neuroscience, network science, AI and data science in addition to endeavours from psychology, linguistics, medicine and the humanities.

Aim. Within the above mosaic of cognitive research lines, it is difficult for researchers to be aware of results recently obtained by colleagues in other fields, thus creating a fragmented landscape of research achievements and gaps. Complexity and Cognition aims at creating new opportunities for research exchange, constructive feedback and paper dissemination among researchers interested in cognition and complexity science. This online symposium addresses the need for transdisciplinary researchers working on cognition to overcome discipline-restrictive boundaries, showcasing the benefits and value that systems thinking and complexity science can bring to the cognitive sciences.

Scope. Complexity and Cognition aligns with the scope of the Conference on Complex Systems (CCS) and will reach multiple relevant research communities of interest for CCS, across cognitive, data, complexity and network sciences

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Complex networks of dynamical agents are widely used to model the behavior of large physical or virtual systems. Unfortunately, due to the often abstract nature of such networks or the size thereof, it is sometimes difficult to assess correctly their structure and parameters. With the ever increasing amount of data accessible nowadays, it is natural to attempt to recover structural information of the system from measurements.

Altogether, there are two overlapping questions that we would like to treat in this symposium:
* What networks characteristics can be recovered from time-series measurements of its agents?
* How to identify and locate disturbances from time-series measurements?

Read the full article at: www.delabaysrobin.site