K-truss, K-type truss

A truss in which the arrangement of the panels, 7 has the appearance of the letter K.
McGraw-Hill Dictionary of Architecture and Construction. Copyright © 2003 by McGraw-Hill Companies, Inc.
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Many approaches have been proposed to find a subgraph that contains the query node, and the goodness metric is maximized or minimized, such as k-core [6, 9, 10], k-truss [11, 12] and densest graph [13].
In this paper, instead of introducing a new goodness metric for community searches like k-core [9] or k-truss [10], we consider the problem of a community search from a new point view: local distance dynamics.
There are many different definitions of cohesive subgraphs in the literature, including maximal clique [19], k-core [20], k-truss [21], and maximal k-edge connected subgraph [22].
In recent years, several metric-based community search models have been studied, such as k-core [9], k-truss [12], and k-influential community [10].
The k-core and k-truss algorithms belong to the first class and are required to optimize both internal denseness and external sparseness.
It can be seen that the K-Truss method is better than other methods on most networks.
(2) For K-Truss, we find that K-Truss has better results and stability than the K-Core algorithm on real-world networks.
It is important to note that the K-Core runs faster than K-Truss and K-Hop, but its accuracy is low.
The K-Truss method has high relative density and slightly outperforms other algorithms.
Comparing three algorithms, we can find that K-Truss and K-Hop have the better value and stability.
The K-Core and K-Truss are very close, and the accuracy of K-Truss is next.
The K-Truss algorithm also uses a local search procedure but has to compute the support of each edge on the whole graph; thus, it has a much longer running time than K-Core.