Exact(7)
The degree centrality method is very simple but of little relevance.
Hence, we need a graph centrality method that ranks keyterms by considering both the semantic structure of the keyterm graph and the frequency of keyterms.
Nevertheless, it is clear that the clustering coefficient results have implications for streamflow predictions, especially when using methods that are based on temporal dependence, and also highlight the potential role of long-term climate change/variability, thus providing support to the results from the degree centrality method.
Considering that there are also some differences between the few years identified in the degree centrality method and those identified in the clustering coefficient method, what makes such years highly significant (or almost insignificant) in the network or very strongly (or very weakly) connected in the network is unclear.
The result of eigenvector centrality is as successful as degree centrality method with 95% of the top ranked 20 genes having supportive evidence.
After each network was constructed, we apply the CINC centrality method to identify the most significant hubs and other effects in each network.
Similar(53)
Consequently, the vulnerabilities estimated through robustness analysis can ascertain those by the classical centrality methods when they appear on both analysis results.
Relative to conventional community graph centrality methods for vertex selection, we demonstrate a substantially lower sampling (network) cost and higher data dissemination rate, on actual Facebook and Internet topology data.
It can handle large graphs very well, and provides functions for generating random and regular graphs, graph visualization, centrality methods and much more.
Leverage is unique from existing centrality methods in that it does not assume serial transportation of information, but rather allows for parallel processing such as that occurring in the brain.
Then we compare ION with eight other existing centrality methods.
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