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Network Science and Graph Analytics are rapidly growing fields that study the structure, behavior, and dynamics of complex networks, including social networks, biological networks, transportation networks, and more. International research awards in Network Science and Graph Analytics aim to recognize and honor outstanding contributions to these fields, and to promote innovation and excellence in research.

Scale-Free Networks: Networks in which a few nodes (hubs) have a disproportionately large number of connections, while the majority of nodes have only a few connections. This distribution of connections follows a power-law, making these networks highly resilient to random failures but susceptible to targeted attacks on hubs.

Random Graph Models: Mathematical models used to generate random networks with specific characteristics, helping researchers understand the properties of real-world networks by comparison.

Community Detection: The process of identifying clusters or communities of nodes that are more densely connected to each other than to the rest of the network. Community detection helps uncover functional modules in complex systems.

Dynamics on Networks: Studying how processes unfold on network structures, such as disease spread, information diffusion, opinion formation, and influence propagation.

Network Visualization: Techniques to represent complex networks visually, making it easier to understand their structure and patterns.

Network science has contributed significantly to our understanding of complex systems and has practical applications in fields such as social network analysis, recommendation systems, epidemiology, supply chain management, and more. Researchers continue to explore and develop new methods to gain insights into the behavior and organization of various real-world systems using network science.


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