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The proposed outlier edge detection algorithms are based on the clustering property of graphs.
Next, we show how the expansion property of graphs leads to such conclusion.
This result agrees with the fact that both the ER model and the PA model algorithms use the clustering property of graphs.
The global clustering coefficient (GCC) and the average local clustering coefficient (ALCC) are the de facto measures of the clustering property of graphs.
A key property of graphs is their degree distribution.
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If you do try to find the solution strategy, it may be helpful to know that the graphs used in this problem are generalized Petersen graphs, which are commonly used to explore general properties of graphs in graph theory.
Graph theory is a branch of discrete combinatorial mathematics that studies the properties of graphs.
In this paper we study containment properties of graphs in relation with the Cartesian product operation.
Rigid graph theory is concerned with properties of graphs that ensure that the network modeled by the graph is rigid.
The spectral radius, the least eigenvalue, and the spread are the most important spectral properties of graphs, which are also the corn of spectral graph theory.
The scale-free properties of graphs generated using a preferential attachment become evident for large graphs, e.g. for the case N → ∞.
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