Let G be a split cluster graph, that is, every connected component is a split graph. For each connected component, we run an algorithm by Heggernes and Kratsch [ 23 ] that checks in linear time whether a graph is a split graph, and if not, produces a 2K 2, C 4, or C 5.

A country with rapid economic, political strength, lacking cultural power, which has the strength of consider as a core.

The reduction works as follows; we assume that the original instance does not contain isolated vertices. Sociologically peripheralization is about spatial disadvantages and micro-scale neighbourhoods struggling with disadvantages and poverty.

Furthermore, there is a clear trade-off between model complexity, algorithmic feasibility of models, and interpretability. In both models detailed here, we assume that all proteins of each core interact with each other; this implies that each core is a clique.

These theories were extended by the concept of growth poles and growth centres and were used also on the global scale. The correlation measure can indicate an area in which to focus and the other measures can be used to fine tune the measure to identify a core size.

Core countries are countries with global scale companies' headquarters, like Apple, and owns the products dealing the last process of production of goods, or providing services and selling goods to semi-periphery and periphery countries. In this model, we thus assume that the cores are disjoint cliques and the vertices of the periphery are an independent set.

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How Can We Distinguish Core and Periphery Area of world?