Reach On Graph Matching Algorithms Based On The Graph Embedded

As the social progress and the rapid development of science and technology, theinformation in the Internet age has skyrocketed. Computer data are faced with massivedata processing and data diversity enormous challenges. Data expressed in Fig datastructures, more data is playing the role. Map data is discrete mathematics andcomputer science important data structure in which graph matching problem in manyareas have been greatly attention. Currently the graph matching method is applied tothe problem of building information modeling retrieval few studies, architectural spaceis connected into the form of performance graph topology. However, graph matchingproblem is a NP-hard problem, people made many based on statistical distribution,pattern recognition, the theory of intelligent optimization algorithms to solve theproblem. Overall, the problem is not completely solved, Individually advantages anddisadvantages of each method.In this paper, We are research the relationship between some of the majoralgorithms and their characteristics of existed. In the analysis of the existing graphembedding method for graph matching problem, based on the vector space explorationfor orthogonal graph embedding algorithm, to establish the best set of orthogonalvector space method, so that the original algorithm is effective and applicable isimproved. Improved algorithm combined with multi-dimensional vector spacecoordinate system established set of ideas that will map to quantify, orthogonal andquantitative expression vector space embedded into focus, quickly get the mosteffective match combinations. In the graph nodes corresponding matching problem in the optimization process for retrieval and other issues facing the combinatorialexplosion, the paper in the matching process using genetic algorithm approachcombined with graph embedding algorithms similar to solve the matching problem. Sothat it is improved that the overall effectiveness of the search and the matchingaccuracy.