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In graph theory, the girth of an undirected graph is the length of a shortest cycle contained in the graph. [1] If the graph does not contain any cycles (that is, it is a forest), its girth is defined to be infinity. [2] For example, a 4-cycle (square) has girth 4. A grid has girth 4 as well, and a triangular mesh has girth 3.
Grit may be domain-specific rather than a domain-general trait. [15] One prolific area of research looked at its role in second language learning or L2 [definition needed] grit . [16] Grit is not only domain-specific, but also context-dependent, with its predictors differing in face-to-face vs. online learning contexts. [17]
Mental toughness is a measure of individual psychological resilience and confidence that may predict success in sport, education, and in the workplace. [1] The concept emerged in the context of sports training and sports psychology, as one of a set of attributes that allow a person to become a better athlete and able to cope with difficult training and difficult competitive situations and ...
Grit is also on full display in the melee of charges against former president Trump. In each case, Trump seems to wish to push through the charges, essentially using grit as a sole defense against ...
A similar problem is finding induced subgraphs in a given graph. Again, some important graph properties are hereditary with respect to induced subgraphs, which means that a graph has a property if and only if all induced subgraphs also have it. Finding maximal induced subgraphs of a certain kind is also often NP-complete. For example:
The repertory grid is a technique for identifying the ways that a person construes (interprets or gives meaning to) his or her experience. [4] It provides information from which inferences about personality can be made, but it is not a personality test in the conventional sense.
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Information about the relative importance of nodes and edges in a graph can be obtained through centrality measures, widely used in disciplines like sociology. For example, eigenvector centrality uses the eigenvectors of the adjacency matrix corresponding to a network, to determine nodes that tend to be frequently visited.