Fast and Accurate Determination of Graph Node Connectivity Leveraging Approximate Methods
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 12742 LNCS, Page: 500-513
2021
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Conference Paper Description
For an undirected graph G, the node connectivity K is defined as the minimum number of nodes that must be removed to make the graph disconnected. The determination of K is a computationally demanding task for large graphs since even the most efficient algorithms require many evaluations of an expensive max flow function. Approximation methods for determining K replace the max flow function with a much faster algorithm that gives a lower bound on the number of node independent paths, but this frequently leads to an underestimate of K. We show here that with minor changes, the approximate method can be adapted to retain most of the performance benefits while still guaranteeing an accurate result.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85111392054&origin=inward; http://dx.doi.org/10.1007/978-3-030-77961-0_41; https://link.springer.com/10.1007/978-3-030-77961-0_41; https://dx.doi.org/10.1007/978-3-030-77961-0_41; https://link.springer.com/chapter/10.1007/978-3-030-77961-0_41
Springer Science and Business Media LLC
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