The phrase "adjacency structure" is correct and usable in written English.
It can be used in contexts related to linguistics, mathematics, or computer science, where it refers to the arrangement of elements that are next to each other.
Example: "In analyzing the sentence, we must consider the adjacency structure of the words to understand their relationships."
Alternatives: "proximity arrangement" or "neighboring configuration".
To accommodate this, a network of sites/nodes is assumed where dependencies among adjacent sites are modelled with spatial correlations and edges between sites determine the adjacency structure of the network.
Depending on the type of adjacency structure and delay between harvests, the opportunity cost of the restrictions, estimated by the change in discounted sum of producer and consumer surplus in the regional log market, ranged from 0.25% to 66% (or US $60 million to $15.3 billion) of the unconstrained value.
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 adjacency structure between any two orthants reflects the edges shared by the two corresponding topologies and permits the definition of paths visiting several orthants.
31 These random effects typically take the form of a conditional autoregression (CAR), which introduces spatial dependence through the adjacency structure of areal units.
Because of these inevitable mistakes, edges may be wrongly removed or kept, thereby changing the adjacency structure and affecting the edges that are considered for deletion and the CI tests that are further performed.
Constructing a human metagenomic network requires several sequential steps: (i) estimating pairwise similarity (or distance) measures between different taxa, (ii) adjacency matrix construction, (iii) network structure (module) detection and differentiated networks.
These include centrality, clustering, adjacency, distance, community structure, bipartivity, subgraphs, and communicability, among others.
Time scales relevant to realistic experimental conditions are achieved with an original Monte-Carlo algorithm that enacts rare events in a stochastic manner using the structure adjacency information.
We also assume that the infection states of a subset of nodes are known and the full network structure (adjacency matrix) is available.
This implementation relies on two important software design elements that will be discussed briefly: (1) clustering is represented as cluster membership lists (CMLs) that resembles the well-known adjacency list data structure, (2) set operations are utilized on the CMLs to efficiently compute the values of TP, FP, TN and FN.
