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In computing, the network model is a database model conceived as a flexible way of representing objects and their relationships. Its distinguishing feature is that the schema , viewed as a graph in which object types are nodes and relationship types are arcs, is not restricted to being a hierarchy or lattice .
The graph convolutional network (GCN) was first introduced by Thomas Kipf and Max Welling in 2017. [9] A GCN layer defines a first-order approximation of a localized spectral filter on graphs. GCNs can be understood as a generalization of convolutional neural networks to graph-structured data. The formal expression of a GCN layer reads as follows:
In mathematics, computer science and network science, network theory is a part of graph theory.It defines networks as graphs where the vertices or edges possess attributes. . Network theory analyses these networks over the symmetric relations or asymmetric relations between their (discrete) compone
The Barabási–Albert (BA) model is an algorithm for generating random scale-free networks using a preferential attachment mechanism. Several natural and human-made systems, including the Internet, the World Wide Web, citation networks, and some social networks are thought to be approximately scale-free and certainly contain few nodes (called hubs) with unusually high degree as compared to ...
In the context of network theory, a complex network is a graph ... In 1998, Duncan J. Watts and Steven Strogatz published the first small-world network model, ...
In network science, the Configuration Model is a family of random graph models designed to generate networks from a given degree sequence. Unlike simpler models such as the Erdős–Rényi model , Configuration Models preserve the degree of each vertex as a pre-defined property.
The Watts and Strogatz model is a random graph generation model that produces graphs with small-world properties. An initial lattice structure is used to generate a Watts–Strogatz model. Each node in the network is initially linked to its closest neighbors. Another parameter is specified as the rewiring probability.
Biological networks, including animal brains, exhibit a high degree of modularity. However, modularity maximization is not statistically consistent, and finds communities in its own null model, i.e. fully random graphs, and therefore it cannot be used to find statistically significant community structures in empirical networks.
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