The Maximum Degree-and-Diameter-Bounded Subgraph problem (MaxDDBS) is a problem in graph theory.
Given a connected host graph , an upper bound for the degree , and an upper bound for the diameter , we look for the largest subgraph of with maximum degree at most and diameter at most . [1]
This problem is also referred to as the Degree-Diameter Subgraph Problem, as it contains the degree diameter problem as a special case (namely, by taking a sufficiently large complete graph as a host graph). Despite being a natural generalization of the Degree-Diameter Problem, MaxDDBS only began to be investigated in 2011, while research in the Degree-Diameter Problem has been active since the 1960s. [1]
There is also a weighted version of the problem (MaxWDDBS) where edges have positive integral weights, and the diameter is measured as the sum of weights along the shortest path. [1]
Regarding its computational complexity, the problem is NP-hard, and not in APX (i.e. it cannot be approximated to within a constant factor in polynomial time). [2] The problem remains NP-hard even when restricting to only one constraint (either degree or diameter). [1]
The Largest Degree-Bounded Subgraph Problem is NP-hard when the subgraph must be connected, while the Maximum Diameter-Bounded Subgraph becomes the maximum clique problem for , which was one of Karp's 21 NP-complete problems. [1]
The order of any graph with maximum degree and diameter cannot exceed the Moore bound: [1]
This bound also serves as a theoretical upper bound for MaxDDBS. If we denote by the order of the largest graph with maximum degree and diameter , then for any solution of MaxDDBS with vertices:
MaxDDBS has diverse practical applications: [1]
A greedy heuristic algorithm has been proposed for MaxWDDBS with a worst-case approximation ratio of , where is the number of vertices in the host graph. [1] The algorithm starts with a -star and grows the subgraph by adding edges incident to live vertices until no more edges can be added while maintaining the degree constraint.
For the diameter-bounded variant alone, an algorithm exists with approximation ratio . [1]
Experimental studies on various host graphs show that the greedy algorithm often performs significantly better than its theoretical worst-case bound suggests, such as on antiprism graphs or random graphs (Watts-Strogatz and Barabási-Albert models). [1]
The problem has been studied for various host graph families, with bounds established for mesh networks, hypercubes, honeycomb networks, triangular networks, butterfly networks, Beneš networks, and oxide networks. [3]
When the host graph is a -dimensional mesh, the problem relates to counting lattice points in balls under the L1 metric. [2]
For a mesh with , the largest subgraph contains as many vertices as lattice points in a closed ball of radius . [2]
The number of lattice points in a maximal ball of radius in dimensions is given by: [2]
Specific constructions have been developed for: [2]
These constructions are asymptotically optimal, with average degree approaching as .
For the -dimensional hypercube , when , there exists a subcube containing a subgraph of order:
This represents the volume of a Hamming ball of radius . [1]