Date and LocationApr 27, 2018 - 1:00pm to 2:00pm
Temporal Branching Approach for Visual Exploration of Simulation Process in Dynamic Networks (presented by James Bird, Computer Science)
Mukhina, K., Guleva, V., & Karsakov, A. (2016). Temporal Branching Approach for Visual Exploration of Simulation Process in Dynamic Networks. Procedia Computer Science, 101, 407-415.
This paper presents a concept for visualization of simulation processes in temporal networks. Сore principles are based on interactive real-time visualization of complex networks and dynamic processes. Any modifications in simulation parameters result in division of a timeline into branches. Described concept was integrated into an extended tool for visual analysis and tested on a model of interbank interaction. Proposed developments significantly improve process of visual analysis: pattern detection, search of distinctions and etc.
Network Neuroscience (presented by Jacob Fisher, Dept of Communication)
Bassett, D. S., & Sporns, O. (2017). Network neuroscience. Nature neuroscience, 20(3), 353.
Despite substantial recent progress, our understanding of the principles and mechanisms underlying complex brain function and cognition remains incomplete. Network neuroscience proposes to tackle these enduring challenges. Approaching brain structure and function from an explicitly integrative perspective, network neuroscience pursues new ways to map, record, analyze and model the elements and interactions of neurobiological systems. Two parallel trends drive the approach: the availability of new empirical tools to create comprehensive maps and record dynamic patterns among molecules, neurons, brain areas and social systems; and the theoretical framework and computational tools of modern network science. The convergence of empirical and computational advances opens new frontiers of scientific inquiry, including network dynamics, manipulation and control of brain networks, and integration of network processes across spatiotemporal domains. We review emerging trends in network neuroscience and attempt to chart a path toward a better understanding of the brain as a multiscale networked system.