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Network science is an interdisciplinary field that studies complex networks of various types and phenomena. Learn about its origins, methods, models, and examples from mathematics, physics, computer science, sociology, and more.
Network formation is an aspect of network science that seeks to model how a network evolves by identifying which factors affect its structure and how these mechanisms operate. Network formation hypotheses are tested by using either a dynamic model with an increasing network size or by making an agent-based model to determine which network ...
A complex network is a graph with non-trivial topological features, such as scale-free degree distribution, high clustering coefficient, and community structure. Learn about the definition, features and examples of complex networks, and how they differ from simple networks such as lattices and random graphs.
Transformer is a neural network that uses multi-head attention to process sequences of tokens, such as words or characters. It was introduced in 2017 as an improvement over recurrent neural networks for machine translation, and has since been applied to various tasks in natural language processing, computer vision, and more.
Mathematically, a neuron's network function () is defined as a composition of other functions (), that can further be decomposed into other functions. This can be conveniently represented as a network structure, with arrows depicting the dependencies between functions.
There are two main intuitions behind the definition of core–periphery network structures; one assumes that a network can only have one core, whereas the other allows for the possibility of multiple cores. These two intuitive conceptions serve as the basis for two modes of core–periphery structures.
Distributed computing is a field of computer science that studies distributed systems, which are computer systems whose inter-communicating components are located on different networked computers. Learn about the challenges, examples, patterns, history and architectures of distributed computing.
Modularity is a measure of the strength of division of a network into modules or communities. It is the fraction of the edges that fall within the groups minus the expected fraction if edges were distributed at random.