Dense subgraph

An example of a graph G with density d_{G}=1.375 and its densest subgraph induced by the vertices b,c,d,e and h in red with density 1.4

In computer science the notion of highly connected subgraphs appears frequently. This notion can be formalized as follows. Let G=(E,V) be an undirected graph and let S=(E_{S},V_{S}) be a subgraph of G. Then the density of S is defined to be d(S)={|E_{S}| \over |V_{S}|}.

The densest subgraph problem is that of finding a subgraph of maximum density. In 1984, Andrew V. Goldberg developed a polynomial time algorithm to find the maximum density subgraph using a max flow technique.

Densest k subgraph

There are many variations on the densest subgraph problem. One of them is the densest k subgraph problem, where the objective is to find the maximum density subgraph on exactly k vertices. This problem generalizes the clique problem and is thus NP-hard in general graphs. There exists a polynomial algorithm approximating the densest k subgraph within a ratio of n^{1/4+\epsilon } for every \epsilon >0,[1] while it does not admit PTAS unless NP\subseteq \bigcap _{\epsilon >0}BPTIME(2^{n^{\epsilon }}).[2] The problem remains NP-hard in bipartite graphs and chordal graphs but is polynomial for trees and split graphs.[3] 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.[4]

Densest at most k subgraph

The objective of the densest at most k problem is to find the maximum density subgraph on at most k vertices. Anderson 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.

Densest at least k subgraph

The densest at least k problem is defined similarly to the densest at most k subgraph problem. There is a 2-approximation due to Anderson. It is also NP-complete.[5]

K-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.

References

  1. Bhaskara, Aditya; Charikar, Moses; Chlamtáč, Eden; Feige, Uriel; Vijayaraghavan, Aravindan (2010), "Detecting high log-densities—an O(n1/4) approximation for densest k-subgraph", STOC'10—Proceedings of the 2010 ACM International Symposium on Theory of Computing, ACM, New York, pp. 201–210, doi:10.1145/1806689.1806719, MR 2743268.
  2. Khot, Subhash (2006), "Ruling out PTAS for graph min-bisection, dense k-subgraph, and bipartite clique", SIAM Journal on Computing 36 (4): 1025–1071, doi:10.1137/S0097539705447037, MR 2272270, CiteSeerX: 10.1.1.114.3324.
  3. Corneil, D. G.; Perl, Y. (1984), "Clustering and domination in perfect graphs", Discrete Applied Mathematics 9 (1): 27–39, doi:10.1016/0166-218X(84)90088-X, MR 754426.
  4. Keil, J. Mark; Brecht, Timothy B. (1991), "The complexity of clustering in planar graphs" (PDF), Journal of Combinatorial Mathematics and Combinatorial Computing 9: 155–159, MR 1111849.
  5. Khuller, Samir; Saha, Barna (2009), "On finding dense subgraphs" (PDF), Automata, Languages and Programming: 36th International Colloquium, ICALP 2009, Rhodes, Greece, July 5-12, 2009, Proceedings, Part I, Lecture Notes in Computer Science 5555, Berlin: Springer-Verlag, pp. 597–608, doi:10.1007/978-3-642-02927-1_50, MR 2544878

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