Network Graph Theory and Organization Model Analysis based on Mathematical Modeling with the Dynamic Systematic Data Perspective

Network graph theory and organization model analysis based on the mathematical modeling with the dynamic systematic data perspective is studied in the paper. As a new attempt of classification organization of network information resources, free classification has brought new theory and supplement to the classification. In the modern Internet era, free classification has a broad space for use and also development. Compared with the traditional classification, it is more suitable for the classification of network information resources. Hence, for the better study, the mathematical model based graph set will be studied. Through the analysis of the general existing network classification system, it is found that there are still many problems to be improved in the classification system used for network information organization, hence, this research study utilizes the dynamic model to improve the accuracy. The performance of the proposed model is evaluated by using the systematic testing.

I. Introduction

Co mparing the governing sets of graphs and the attribute reduction method of decision tables, it is not difficult to find that they can be obtained by constructing Boolean functions for logical operations, which shows that it is possible to study related problems in graph theory based on rough set theory. A rough set solution method for some related problems in graph theory is proposed [1]–[4]. This paper uses the adjacency matrix to cons truct the induced decis ion tab le for undirected graph, and proposes a heuristic minimum dominating set approximation algorithm based on rough set theory [5], [6]. The algorithm uses forward and backward search mechanism, not only can obtain the minimal dominating set of the graph, but also can obtain a better approximate minimal dominating set.