Dense graph
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In mathematics, a dense graph is a graph in which the number of edges is close to the maximal number of edges (where every pair of vertices is connected by one edge). The opposite, a graph with only a few edges, is a sparse graph. The distinction of what constitutes a dense or sparse graph is ill-defined, and is often represented by 'roughly equal to' statements. Due to this, the way that density is defined often depends on the context of the problem.
The graph density of simple graphs is defined to be the ratio of the number of edges Template:AbsScript error: No such module "Check for unknown parameters". with respect to the maximum possible edges.
For undirected simple graphs, the graph density is:
For directed, simple graphs, the maximum possible edges is twice that of undirected graphs (as there are two directions to an edge) so the density is:
where Template:Mvar is the number of edges and Template:Mvar is the number of vertices in the graph. The maximum number of edges for an undirected graph is , so the maximal density is 1 (for complete graphs) and the minimal density is 0.Template:Sfn
For families of graphs of increasing size, one often calls them sparse if as . Sometimes, in computer science, a more restrictive definition of sparse is used like or even . In this same context, a dense graph may be defined as any graph where Template:AbsScript error: No such module "Check for unknown parameters". is "close" to .[1][2]
Upper density
Upper density is an extension of the concept of graph density defined above from finite graphs to infinite graphs. Intuitively, an infinite graph has arbitrarily large finite subgraphs with any density less than its upper density, and does not have arbitrarily large finite subgraphs with density greater than its upper density. Formally, the upper density of a graph Template:Mvar is the infimum of the values α such that the finite subgraphs of Template:Mvar with density α have a bounded number of vertices. It can be shown using the Erdős–Stone theorem that the upper density can only be 1 or one of the superparticular ratios 0, Template:Sfrac, Template:Sfrac, Template:Sfrac, Template:Sfrac, … Template:SfracScript error: No such module "Check for unknown parameters".[3]
Sparse and tight graphs
Script error: No such module "Footnotes". and Script error: No such module "Footnotes". define a graph as being (k, l)Script error: No such module "Check for unknown parameters".-sparse if every nonempty subgraph with Template:Mvar vertices has at most kn − lScript error: No such module "Check for unknown parameters". edges, and (k, l)Script error: No such module "Check for unknown parameters".-tight if it is (k, l)Script error: No such module "Check for unknown parameters".-sparse and has exactly kn − lScript error: No such module "Check for unknown parameters". edges. Thus trees are exactly the (1,1)Script error: No such module "Check for unknown parameters".-tight graphs, forests are exactly the (1,1)Script error: No such module "Check for unknown parameters".-sparse graphs, and graphs with arboricity Template:Mvar are exactly the (k,k)Script error: No such module "Check for unknown parameters".-sparse graphs. Pseudoforests are exactly the (1,0)Script error: No such module "Check for unknown parameters".-sparse graphs, and the Laman graphs arising in rigidity theory are exactly the (2,3)Script error: No such module "Check for unknown parameters".-tight graphs.[4]
Other graph families not characterized by their sparsity can also be described in this way. For instance the facts that any planar graph with Template:Mvar vertices has at most 3n – 6Script error: No such module "Check for unknown parameters". edges (except for graphs with fewer than 3 vertices), and that any subgraph of a planar graph is planar, together imply that the planar graphs are (3,6)Script error: No such module "Check for unknown parameters".-sparse. However, not every (3,6)Script error: No such module "Check for unknown parameters".-sparse graph is planar. Similarly, outerplanar graphs are (2,3)Script error: No such module "Check for unknown parameters".-sparse and planar bipartite graphs are (2,4)Script error: No such module "Check for unknown parameters".-sparse.
Streinu and Theran show that testing (k,l)Script error: No such module "Check for unknown parameters".-sparsity may be performed in polynomial time when Template:Mvar and Template:Mvar are integers and 0 ≤ l < 2kScript error: No such module "Check for unknown parameters"..Template:Sfn
For a graph family, the existence of Template:Mvar and Template:Mvar such that the graphs in the family are all (k,l)Script error: No such module "Check for unknown parameters".-sparse is equivalent to the graphs in the family having bounded degeneracy or having bounded arboricity. More precisely, it follows from a result of Script error: No such module "Footnotes". that the graphs of arboricity at most Template:Mvar are exactly the (a,a)Script error: No such module "Check for unknown parameters".-sparse graphs.Template:Sfn Similarly, the graphs of degeneracy at most Template:Mvar are -sparse graphs.Template:Sfn
Sparse and dense classes of graphs
Script error: No such module "Footnotes". considered that the sparsity/density dichotomy makes it necessary to consider infinite graph classes instead of single graph instances. They defined somewhere dense graph classes as those classes of graphs for which there exists a threshold t such that every complete graph appears as a t-subdivision in a subgraph of a graph in the class. To the contrary, if such a threshold does not exist, the class is nowhere dense.[5]
The classes of graphs with bounded degeneracy and of nowhere dense graphs are both included in the biclique-free graphs, graph families that exclude some complete bipartite graph as a subgraph.Template:Sfn
See also
Notes
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- ↑ Script error: No such module "Footnotes". and Script error: No such module "Footnotes".
- ↑ Script error: No such module "Footnotes".. Properties of the nowhere dense vs somewhere dense dichotomy are discussed by Script error: No such module "Footnotes"..
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References
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Further reading
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