Sentence examples for mean node adjacency from inspiring English sources

For simulated networks with low mean node adjacency (i.e. mostly weak connections among nodes, like most gene co-expression networks), we observed values of cor(K C) approaching 1, indicating a nearly perfect positive linear relationship between Z.K and Z.C.

As the strength of connections among nodes (i.e. mean node adjacency) began to increase, cor(K C) began to shift, while also revealing a dependence on network size (i.e. number of nodes; Figure 3).

To test this hypothesis, we conducted a simulation study to explore the properties of cor(K C) by systematically varying the network topology (mean node adjacency) and network size (number of nodes).

1) A random graph is represented by a node adjacency matrix A(n × n).

Through bus types and the node adjacency matrix, we can determine the type of each line.

The node adjacency list is a compact representation and allows easy traversal along the outgoing links.

To manipulate a bidirected string graph in MapReduce, we use a node adjacency list to represent the graph, which stores node id (i.e., the identifier of a node) as the key, and node data structure as the value.

Such concepts include decentralization and homogeneity, as well as summaries of node-based measures such as the mean correlation, mean connectivity, mean clustering coefficient, mean intersample adjacency (or density), and mean maximum adjacency ratio (MAR).

In sample networks, we often refer to the density as the mean intersample adjacency (ISA).

Thus, the mean adjacency is roughly equal to the mean correlation in sample networks.

The mean adjacency matrix of this ensemble is then calculated.