Dense subgraph explained

In graph theory and computer science, a dense subgraph is a subgraph with many edges per vertex. This is formalized as follows: let be an undirected graph and let be a subgraph of . Then the density of is defined to be:

d(S) = {|E _{S|\over|V} _{S|}}

The densest subgraph problem is that of finding a subgraph of maximum density. The density of the maximally dense subgraph of a graph is sometimes referred to as its subgraph density. In 1984, Andrew V. Goldberg developed a polynomial time algorithm to find the maximum density subgraph using a max flow technique. This has been improved by Gallo, Grigoriadis and Tarjan in 1989 to run in time. A simple LP for finding the optimal solution was given by Charikar in 2000.

Subgraph density is asymptotic to the related notion of arboricity and to graph degeneracy.

Densest subgraph

There are many variations on the densest subgraph problem. One of them is the densest subgraph problem, where the objective is to find the maximum density subgraph on exactly vertices. This problem generalizes the clique problem and is thus NP-hard in general graphs. There exists a polynomial algorithm approximating the densest subgraph within a ratio of

n ^{1/4} for every \epsilon > 0 ,^[1] while it does not admit an n ^{1/\operatorname{polyloglog} n} -approximation in polynomial time unless the exponential time hypothesis is false.^[2] Under a weaker assumption that NP \nsubseteq c ap _{\epsilon} BPTIME ( 2 ^{n ^{\epsilon}} ) , no PTAS exists for the problem.^[3]

The problem remains NP-hard in bipartite graphs and chordal graphs but is polynomial for trees and split graphs.^[4] It is open whether the problem is NP-hard or polynomial in (proper) interval graphs and planar graphs; however, a variation of the problem in which the subgraph is required to be connected is NP-hard in planar graphs.^[5]

Densest at most subgraph

The objective of the densest at most

k problem is to find the maximum density subgraph on at most k vertices. Andersen and Chellapilla showed that if there exists an \alpha -approximation for this problem then that will lead to an \Theta(\alpha ^{2)} -approximation for the densest k subgraph problem.^[6] Later, this was improved by Khuller and Saha who showed that an \alpha -approximation for densest at most k subgraph implies a 4 \alpha -approximation for the densest k subgraph problem.

Densest at least subgraph

The densest at least

k problem is defined similarly to the densest at most k subgraph problem. The problem is NP-complete, but admits 2-approximation in polynomial time. Moreover, there is some evidence that this approximation algorithm is essentially the best possible: assuming the small set expansion hypothesis (a computational complexity assumption closely related to the unique games conjecture), then it is NP-hard to approximate the problem to within ( 2 - \epsilon) factor for every constant \epsilon > 0 .

-clique densest subgraph

Charalampos Tsourakakis introduced the

k -clique densest subgraph problem. This variation of the densest subgraph problem aims to maximize the average number of induced k cliques d _{k} (S) = {|C _{k(S)|\over|V} _{S|}} , where C _{k(S)} is the set of k -cliques induced by S . Notice that the densest subgraph problem is obtained as a special case for k =2 . This generalization provides an empirically successful poly-time approach for extracting large near-cliques from large-scale real-world networks.

Locally top- densest subgraph

Qin et al. introduced the problem of top-k locally densest subgraphs discovery in a graph, each of which achieves the highest density in its local region in the graph: it is neither contained in any supergraph with the same or larger density, nor it contains subgraphs with density being loosely connected with the rest of the local densest subgraph. Note that the densest subgraph problem is obtained as a special case for

k =1 . The set of locally densest subgraphs ina graph can be computed in polynomial time.

Further reading

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Notes and References

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6. Andersen . Reid . Chellapilla . Kumar . 2009 . Avrachenkov . Konstantin . Donato . Debora . Litvak . Nelly . Finding Dense Subgraphs with Size Bounds . Algorithms and Models for the Web-Graph . Lecture Notes in Computer Science . en . Berlin, Heidelberg . Springer . 25–37 . 10.1007/978-3-540-95995-3_3 . 978-3-540-95995-3.