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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:
Networks such as the previous one are commonly called feedforward, because their graph is a directed acyclic graph. Networks with cycles are commonly called recurrent . Such networks are commonly depicted in the manner shown at the top of the figure, where f {\displaystyle \textstyle f} is shown as dependent upon itself.
It does so by interpolating between a randomized structure close to ER graphs and a regular ring lattice. Consequently, the model is able to at least partially explain the "small-world" phenomena in a variety of networks, such as the power grid, neural network of C. elegans, networks of movie actors, or fat-metabolism communication in budding ...
Both directed acyclic graphs and undirected graphs are special cases of chain graphs, which can therefore provide a way of unifying and generalizing Bayesian and Markov networks. [3] An ancestral graph is a further extension, having directed, bidirected and undirected edges. [4] Random field techniques
Exponential Random Graph Models (ERGMs) are a family of statistical models for analyzing data from social and other networks. [1] [2] Examples of networks examined using ERGM include knowledge networks, [3] organizational networks, [4] colleague networks, [5] social media networks, networks of scientific development, [6] and others.
There are two closely related variants of the Erdős–Rényi random graph model. A graph generated by the binomial model of Erdős and Rényi (p = 0.01)In the (,) model, a graph is chosen uniformly at random from the collection of all graphs which have nodes and edges.
Neural Network Exchange Format (NNEF) is an artificial neural network data exchange format developed by the Khronos Group. It is intended to reduce machine learning deployment fragmentation by enabling a rich mix of neural network training tools and inference engines to be used by applications across a diverse range of devices and platforms.
Estimating the power-law exponent of a scale-free network is typically done by using the maximum likelihood estimation with the degrees of a few uniformly sampled nodes. [14] However, since uniform sampling does not obtain enough samples from the important heavy-tail of the power law degree distribution, this method can yield a large bias and a ...