Dense 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 ${\displaystyle n^{1/4\,+\,\epsilon }}$ for every ${\displaystyle \epsilon >0}$,[3] while it does not admit an ${\displaystyle n^{1/\!\operatorname {polyloglog} n}}$-approximation in polynomial time unless the exponential time hypothesis is false.[4] Under a weaker assumption that ${\displaystyle {\mathsf {NP}}\nsubseteq \bigcap _{\epsilon >0}{\mathsf {BPTIME}}(2^{n^{\epsilon }})}$, no PTAS exists for the problem.[5]

The problem remains NP-hard in bipartite graphs and chordal graphs but is polynomial for trees and split graphs.[6] 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.[7]

The objective of the densest at most ${\displaystyle k}$ problem is to find the maximum density subgraph on at most ${\displaystyle k}$ vertices. Anderson and Chellapilla showed that if there exists an ${\displaystyle \alpha }$-approximation for this problem then that will lead to an ${\displaystyle \Theta (\alpha ^{2})}$-approximation for the densest ${\displaystyle k}$ subgraph problem.

The densest at least ${\displaystyle k}$ problem is defined similarly to the densest at most ${\displaystyle k}$ subgraph problem. The problem is NP-complete,[8] but admits 2-approximation in polynomial time.[9] 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 ${\displaystyle (2-\epsilon )}$ factor for every constant ${\displaystyle \epsilon >0}$.[10]

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

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 ${\displaystyle k=1}$. The set of locally densest subgraphs in a graph can be computed in polynomial time.