Recursive-Parallel Algorithm for Solving the Maximum Common Subgraph Problem

In this paper, we propose an algorithm for solving the maximum common subgraph problem. The sequential and parallel versions of the algorithm and their software implementation are described, and their effectiveness is experimentally studied. This problem is one of the most famous NP-complete problems. Its solution may be required when solving many practical problems related to the study of complex structures. We solve it in a formulation in which we need to find all possible isomorphisms of the found common subgraph. Due to the extremely high complexity of the problem, the desire to speed up its solution by parallelizing the algorithm is quite natural. To organize parallel computing, the RPM_ParLib library is used, which makes it possible to create parallel applications running on a local computer network under the control of the .NET Framework runtime environment. The library supports a recursive-parallel programming style and ensures efficient distribution of work and dynamic load balancing of computing modules during program execution. It can be used for applications written in any programming language supported by the .NET Framework. The purpose of the numerical experiment is to study the acceleration achieved through the recursive-parallel organization of calculations. For the experiment, a special application in C# that is designed to generate various sets of initial data with specified parameters is developed. Here, we describe the characteristics of the generated initial graph pairs and the results obtained during the experiment.

Authors

Institutions

Publication Details

Journal
Automatic Control and Computer Sciences
Published
2024-12-01
DOI
https://doi.org/10.3103/s0146411624700287
Primary Topic
Graph Theory and Algorithms
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Recursive-Parallel Algorithm for Solving the Maximum Common Subgraph Problem

V. V. Vasilchikov
Automatic Control and Computer Sciences
Graph Theory and Algorithms
article

Recursive-Parallel Algorithm for Solving the Maximum Common Subgraph Problem

V. V. Vasilchikov
article en

Abstract

In this paper, we propose an algorithm for solving the maximum common subgraph problem. The sequential and parallel versions of the algorithm and their software implementation are described, and their effectiveness is experimentally studied. This problem is one of the most famous NP-complete problems. Its solution may be required when solving many practical problems related to the study of complex structures. We solve it in a formulation in which we need to find all possible isomorphisms of the found common subgraph. Due to the extremely high complexity of the problem, the desire to speed up its solution by parallelizing the algorithm is quite natural. To organize parallel computing, the RPM_ParLib library is used, which makes it possible to create parallel applications running on a local computer network under the control of the .NET Framework runtime environment. The library supports a recursive-parallel programming style and ensures efficient distribution of work and dynamic load balancing of computing modules during program execution. It can be used for applications written in any programming language supported by the .NET Framework. The purpose of the numerical experiment is to study the acceleration achieved through the recursive-parallel organization of calculations. For the experiment, a special application in C# that is designed to generate various sets of initial data with specified parameters is developed. Here, we describe the characteristics of the generated initial graph pairs and the results obtained during the experiment.

Automatic Control and Computer SciencesVol. 58(7)
Yaroslavl State University (RU)
Openalex Percentile: Top 73%
Graph Theory and Algorithms
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.