The Big Data Networks section is dedicated to publishing research focused on advancing the understanding of complex systems through model-driven and data-driven approaches.

Led by Dr. Nitesh Chawla from the University of Notre Dame, the Big Data Networks section welcomes submissions in the various domains of network science, which address the challenges and opportunities arising from the interaction of diverse complex systems.

Topics considered in the scope of this section include:

biological systems
computational social science
complex systems
deep learning and embedding methods
ethics, fairness, trust, and networks
graph mining
influence and information propagation
information networks
learning on graphs
network medicine
network science
recommendation systems
social networks
statistical relational learning
transportation systems

Submissions should provide detailed, in-depth knowledge about the modeling, analysis, and understanding of networks or graphs in different complex systems.

In particular, the section welcomes submissions which support and advance the understanding of complex systems, network science, and data-driven approaches in biological systems, computational social science, transportation systems, and network medicine, contributing to SDGs 3 (Good Health and Well-being), 9 (Industry, Innovation, and Infrastructure), and 11 (Sustainable Cities and Communities).

The Big Data Networks section does not consider submissions that primarily focus on general management or process optimization without a strong connection to big data analytics, network analysis, or data-driven decision-making. However, studies that emphasize the role of big data or network methodologies in addressing complex problems related to biological systems, computational social science, transportation systems, and network medicine are within the scope of this section.

This multidisciplinary section is at the forefront of disseminating and communicating cutting-edge scientific knowledge and impactful discoveries in the field of network science to researchers, industry, policymakers, and the public worldwide.

Significant EHR Feature-Driven T2D Inference: Predictive Machine Learning and Networks