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1 sks that we compile a listing of all maximal cliques.
2 ce of a network core and the distribution of cliques, (2) global and local binary properties, (3) glo
3 rm a global alignment by employing a maximum clique algorithm on a specially defined graph that we ca
4 h asks us to determine the size of a largest clique, and the maximal clique enumeration problem, whic
5 hies, friendship networks are organized into cliques, and comparative relations (e.g., "bigger than"
7 sional spaces, rings, dominance hierarchies, cliques, and other forms and successfully discovers the
8 e peak matches in a graph, finds the maximal cliques, and then combines cliques with shared peaks to
10 addition, activation patterns of the neural clique assemblies can be converted to strings of binary
11 s of the DMN were identified, as well as the cliques associated with a reduced preference for motor p
12 thm achieved a considerable improvement over clique based algorithms in terms of its ability to recov
14 l approaches, such as mean shortest path and clique-based metrics, have been useful but are not well
15 lustering, to graphical approaches such as k-clique communities, weighted gene co-expression networks
16 elative packing group (RPG) that applies the clique concept from graph theory as a natural basis for
17 ree topologies is computed using the maximal cliques concept, in phase II divergence times for each o
19 a graph clustering problem called the quasi-clique decomposition problem, which has recently also be
20 MATE-CLEVER (Mendelian-inheritance-AtTEntive CLique-Enumerating Variant findER) as an approach that a
22 he size of a largest clique, and the maximal clique enumeration problem, which asks that we compile a
26 elation list can be examined using the novel clique finder tool to determine the sets of genes most l
29 rk analysis, including identifying defective cliques, finding small network motifs (such as feed-forw
30 novel method that employs heaviest weighted clique-finding (HCF), which we show significantly outper
36 framework for motif finding through finding cliques in a graph but have made this framework substant
37 CliXO is a new approach that finds maximal cliques in a network induced by progressive thresholding
38 independent and recurrent SCNA: s as maximal cliques in an interval graph constructed from overlaps b
40 of network-level coding units, termed neural cliques, in the hippocampus has enabled real-time patter
41 onary game theory model, that cooperation on cliques increases linearly with community motif count.
42 milarly to the case of single-node or single-clique initiators studied previously, we observe that co
43 Many traditional graph algorithms such as k-clique, k-coloring, and subgraph matching have great pot
44 ce of algorithms on inherently low-diameter, clique-like benchmarks may not always be indicative of e
46 l scales, one can detect both clique- or non clique-like communities without imposing an upper scale
47 ume an implicit notion of community based on clique-like subgraphs, a form of community structure tha
48 m geometric constraints can have natural non clique-like substructures with large effective diameters
50 thm (QCM algorithm) into edge-covering Quasi-Clique Merger algorithm (eQCM) for mining weighted sub-n
52 s for a set of chromatograms, the Consistent Cliques Method (CCM) represents all peaks from all chrom
53 he protein interaction network for defective cliques (nearly complete complexes of pairwise interacti
54 ically alter the structure of scale-free and clique networks and show, through a stochastic evolution
56 g Monte Carlo simulations, we identified two cliques of co-expressed genes that were significantly en
59 own and widely-studied problems: the maximum clique optimization problem, which asks us to determine
60 ughput network data has been to extract near-cliques or highly dense subgraphs from a single protein-
61 e network at all scales, one can detect both clique- or non clique-like communities without imposing
63 a novel in-house algorithm and a tailor-made Clique Percolation Method to extract linear and nonlinea
64 contribution is the careful selection of the clique potential functions in the MRF so its maximum a p
65 gorithm was implemented to solve the maximal clique problem for a simple graph with six vertices.
68 Although based on solutions to the maximum clique problem, this algorithm deals properly with ambig
70 find an optimal selection of non-overlapping cliques, resulting in a very fast algorithm, which we ca
72 ature has classified these groups as support cliques, sympathy groups, bands, cognitive groups, tribe
74 describe groups of amino acid sites called "cliques" that were highly associated with each other.
75 ve cospiking" dynamics that allow the neural clique to overcome the response variability of its membe
77 a novel approach to matrix analysis, called clique topology, that extracts features of the data inva
78 sponding to the total ensemble of six-vertex cliques was built, followed by a series of selection pro
81 finds the maximal cliques, and then combines cliques with shared peaks to extract reliable features.
83 ied, and behavioral domain analysis of these cliques yielded discrete functional properties, demonstr
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