Sentence examples for measures for graph from inspiring English sources

Degree centrality is one of the simplest and least computation-demanding measures for graph theory complex networks.

In terms of information-theoretic measures for graphs, relatively little attention has been paid to this effort.

Therefore, our main contribution is to shed light on the relatedness between some selected information measures for graphs by performing a large scale analysis using chemical networks.

In the following, we express the main findings of the paper in brief: We explored the relatedness between information measures for graphs.

More precisely, Trucco [12] and Rashevsky [19] defined entropy measures for graphs which were interpreted as the structural information content of a graph; the original information measure due to Rashevsky [19] is called the so-called topological information of a graph in question, see Equation (4).

This algorithm uses the notion of modularity, which is a quality measure for graph clustering proposed by Newman [35].

The diameter of the surviving route graph could be one of the fault-tolerance measures for the graph G and the routing ρ and it is denoted by D R G ,ρ /F.

Moreover, one observes that the sensitivity values of the remaining information measures for this graph class are high.

Thus, the measuring accuracy for graph matching can be improved.

In this paper, we first define some network measures for stochastic graphs, and then we propose four sampling algorithms based on learning automata for stochastic graphs.

The resulting greedy algorithm is shown to yield better results than common centrality measures for large graphs while being computationally competitive with degree-based greedy vertex removal.