Sentence examples for from the adjacency from inspiring English sources

All centrality measures are derived from the adjacency matrix, so they constitute different mathematical computations using the same underlying data.

In this paper we examine the ternary codes obtained from the adjacency (resp. incidence) matrices of these graphs (resp. designs), and those of their corresponding derived and residual designs.

For a set Ω of size n⩾7 and Ω{3} the set of subsets of Ω of size 3, we examine the binary codes obtained from the adjacency matrix of each of the three graphs with vertex set Ω{3}, with adjacency defined by two vertices as 3-sets being adjacent if they have zero, one or two elements in common, respectively.

We show that given a k-bounded pseudo-Boolean function f, one can always compute the cth moment of f over regions of arbitrary radius in Hamming space in polynomial time using algebraic information from the adjacency structure (where k and c are constants).

The number (N_{ind}) of different patterns can be counted from the adjacency matrix (mathrm{K'}), which has elements ([mathrm{K'}]_{i j}= kappa _{i j}) and ([mathrm{K'}]_{i i}=0) (excluding self-appreciations) providing all the information to count the number (N_{ind}) and intensities (W_{ind}) of patterns of interest.

Whereas the predictors in MRF-Deng are derived from the adjacency matrix that represents the network, they are derived from a similarity kernel in KLR.

An interesting extension to study would replace the Laplacian by a random-walk-based matrix, derived from powers of the adjacency matrix, so as to treat disconnected SNPs that are close-by in the networks differently from those that are far apart.

In the latter scheme, diffusion wavelets are based on construction of an orthonormal basis for functions supported on a graph and obtaining a custom-designed basis by analyzing eigenvectors of a diffusion matrix derived from the graph adjacency matrix.

To assess the performance of a clustering (text {C}={U_1,U_2,ldots,U_k}), obtained from the "initial" adjacency matrix (mathbf{W}_0=mathbf{W}), we propose the modified Ncut criterion as follows: (text {Ncut} text {C},mathbf{W}_I)=sum ^k_{p=1} Big (sum _{u in U_p}sum _{v notin U_p}x_{uv}/sum _{ uin U_p}sum _{v in V}x_{uv}Big )).

From the spatial adjacency relations, we calculated the shortest distances between all pairs of areas, Δdist; that is, we determined the minimum number of borders separating any two areas within the cortical parcellation adopted by Scannell and colleagues.

A pair of genes (or orthogroup) were determined as co-expressed if the Spearman's correlation coefficient rho is greater than 0.3 and if the value from the weighted adjacency matrix of the network is greater than 0.01.