Sentence examples for graphs and random from inspiring English sources

We describe several random models, inspired by earlier models for random graphs and random simplicial complexes, and give results on properties such as Hilbert function and Krull dimension.

Topics will include random utility models, item-response theory, ranking and learning to rank, centrality and ranking on graphs, and random graphs.

Secondly, we use Back Tracking to enumerate all sub-graphs of a given size that occur in the input graph which are obtained from real graphs and random graphs.

According to the characteristic of the parity nodes, we cut down the searching space and storage space in real graphs and random graphs, thereby reducing the computational cost of verifying the isomorphism of sub-graphs.

The associated matrix is easy to compress and thus it is easy to know the size of a sub-graph, so we use the associated matrix to store real graphs and random graphs.

Note that this applies equally to natural graphs and random ones (Hence we emphasize that the differences between our results and those of Alon et al. are not explicable solely on the basis of alternative definitions of some of the motifs).

Families of graphs include social networks, small-world graphs, Internet graphs, planar graphs, well-shaped meshes, power-law graphs, and classic random graphs.

The simulations were tested for a regular grid, a uniform random graph, and a random graph with preferential attachment.

Then we apply the method to random graphs and lattice embedded random regular graphs, and derive the expressions of their characteristic curves.

Then we apply this curve shaped description to both random graphs and lattice embedded random regular graphs, and derive the expressions of their curves.

More advanced graph-kernels and random-walk graph kernels [52] [54] can also be applied to DCNs to predict domain functions in order to take non-immediate neighboring domains into account.