## Abstract

A novel combinatorial optimization algorithm called 2-stage discrete optimization method (2DOM) is proposed for the largest common subgraph problem (LCSP) in this paper. Given two graphs G = (V_{1}, E_{1}) and H = (V_{2}. E_{2}), the goal of LCSP is to find a subgraph G′ = (V′_{1}, E′_{1}) of G and a subgraph H′ = (V′_{2},E′_{2}) of H such that G′ and H′ are not only isomorphic to each other but also their number of edges is maximized. The two graphs G′ and H′ are isomorphic when |V′_{1}| = |V′_{2}| and |E′_{1}| = |E′_{2}|, and there exists one-to-one vertex correspondence f : V′_{1} → V′_{2} such that {u, v} ∈ E′_{1} if and only if {f(u),/(w)} ∈ E′_{2}. LCSP is known to be NP-complete in general. The 2DOM consists of a construction stage and a refinement stage to achieve the high solution quality and the short computation time for large size difficult combinatorial optimization problems. The construction stage creates a feasible initial solution with considerable quality, based on a greedy heuristic method. The refinement stage improves it keeping the feasibility, based on a random discrete descent method. The performance is evaluated by solving two types of randomly generated 1200 LCSP instances with a maximum of 500 vertices for G and 1000 vertices for H. The simulation result shows the superiority of 2DOM to the simulated annealing in terms of the solution quality and the computation time.

Original language | English |
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Pages (from-to) | 1145-1153 |

Number of pages | 9 |

Journal | IEICE Transactions on Information and Systems |

Volume | E82-D |

Issue number | 8 |

Publication status | Published - Jan 1 1999 |

Externally published | Yes |

## Keywords

- Common subgraph
- Discrete descent method
- Greedy method
- Isomorphic
- NP-complete
- Simulated annealing

## ASJC Scopus subject areas

- Software
- Hardware and Architecture
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering
- Artificial Intelligence