The Evolving Bipartite Network and Semi-Bipartite Network Models with Adjustable Scale and Hybrid Attachment Mechanisms

The bipartite graph structure exists in the connections of many objects in the real world, and the evolving modeling is a good method to describe and understand the generation and evolution within various real complex networks. Previous bipartite models were proposed to mostly explain the principle of attachments, and ignored the diverse growth speed of nodes of sets in different bipartite networks. In this paper, we propose an evolving bipartite network model with adjustable node scale and hybrid attachment mechanisms, which uses different probability parameters to control the scale of two disjoint sets of nodes and the preference strength of hybrid attachment respectively. The results show that the degree distribution of single set in the proposed model follows a shifted power-law distribution when parameter r and s are not equal to 0, or exponential distribution when r or s is equal to 0. Furthermore, we extend the previous model to a semi-bipartite network model, which embeds more user association information into the internal network, so that the model is capable of carrying and revealing more deep information of each user in the network. The simulation results of two models are in good agreement with the empirical data, which verifies that the models have a good performance on real networks from the perspective of degree distribution. We believe these two models are valuable for an explanation of the origin and growth of bipartite systems that truly exist.

Evolving Network Model with Local-Area Preference for Mobile Ad Hoc Network

To accurately describe the evolving features of Mobile Ad Hoc Networks (MANETs) and to improve the performance of such networks, an evolving topology model with local-area preference is proposed. The aim of the model, which is analyzed by the mean field theory, is to optimize network structures based on users' behaviors in MANETs. The analysis results indicate that the network generated by this evolving model is a kind of scale-free network. This evolving model can improve the fault-tolerance performance of networks by balancing the connectivity and two factors, i.e., the remaining energy and the distance to nodes. The simulation results show that the evolving topology model has superior performance in reducing the traffic load and the energy consumption, prolonging network lifetime and improving the scalability of networks. It is an available approach for establishing and analyzing actual MANETs.

A local-world evolving hypernetwork model

Complex hypernetworks are ubiquitous in the real system. It is very important to investigate the evolution mecha- nisms. In this paper, we present a local-world evolving hypernetwork model by taking into account the hyperedge growth and local-world hyperedge preferential attachment mechanisms. At each time step, a newly added hyperedge encircles a new coming node and a number of nodes from a randomly selected local world. The number of the selected nodes from the local world obeys the uniform distribution and its mean value is m. The analytical and simulation results show that the hyperdegree approximately obeys the power-law form and the exponent of hyperdegree distribution is 7 = 2 ＋ 1/m. Furthermore, we numerically investigate the node degree, hyperedge degree, clustering coefficient, as well as the average distance, and find that the hypemetwork model shares the scale-flee and small-world properties, which shed some light for deeply understanding the evolution mechanism of the real systems.

A Collaboration Network Model with Multiple Evolving Factors

To describe the empirical data of collaboration networks, several evolving mechanisms have been proposed, which usually introduce different dynamics factors controlling the network growth. These models can reasonably reproduce the empirical degree distributions for a number of we11-studied real-world collaboration networks. On the basis of the previous studies, in this work we propose a collaboration network model in which the network growth is simultaneously controlled by three factors, including partial preferential attachment, partial random attachment and network growth speed. By using a rate equation method, we obtain an analytical formula for the act degree distribution. We discuss the dependence of the act degree distribution on these different dynamics factors. By fitting to the empirical data of two typical collaboration networks, we can extract the respective contributions of these dynamics factors to the evolution of each networks.

Product Model Evolvement and Configuration Knowledge Reuse Method

Configuration knowledge is a dynamic information set which is evolving and enriching on and on. Product model is the instantiation of configuration knowledge and the evolution of configuration knowledge is the essential inherent reason which causes the models dynamic evolvement. In the traditional model evolvement process, the inheriting and reuse of configuration knowledge was always ignored. Aim at solving the above problem, the multistage rhombus evolution mode of configuration knowledge is discussed in this paper. The product model based on configuration knowledge is put forward in different levels to achieve the models dynamic evolvement and automatic upgrading. The evolving configuration knowledge drives the product model to evolve directly according to the rule of up-layer evolvement. Furthermore, a new configuration knowledge reuse and optimization technology is presented to inheriting and reuse the foregone configuration knowledge in the course of model evolvement. At last, the air separation equipment which is related with the project is taken as an example to illuminate that the presented model evolvement and configuration knowledge reuse technology are validity and practical.

Model evolvement and reuse technology of injection molding machine based on performance knowledge

To illuminate the necessity of model evolvement and reuse, dynamics of injection molding machine＇s product models are analyzed. The performance knowledge is used to support the model evolvement and reuse. The driven factors of mechanical product model are concluded. The dynamic characteristics of reuse. Finally, HT1800X1N series injection molding machines are taken as examples to illuminate that the arithmetic is correct and practical.

CONCEPTUAL MODELING BASED ON LOGICAL EXPRESSION AND EVOLVEMENT

Aiming at the problem of abstract and polytype information modeling in product conceptual design, a method of conceptual modeling based on logical expression and evolvement is presented. Based on the logic expressions of the product conceptual design information, a fimction/logic/strucmre mapping model is set up. First, the fimction semantics is transformed into logical expressions through fimction/logic mapping. Second, the methods of logical evolvement are utilized to describe the fimction analysis, fimction/strucmre mapping and structure combination. Last, the logical structure scheme is transformed into geometrical sketch through logic/structure mapping. The conceptual design information and modeling process are described uniformly with logical methods in the model, and an effective method for computer aided conceptual design based on the model is implemented.