scispace - formally typeset

Network model

About: Network model is a(n) research topic. Over the lifetime, 9992 publication(s) have been published within this topic receiving 147912 citation(s). more


Proceedings ArticleDOI: 10.1145/1282480.1282492
Peter P. Chen1Institutions (1)
22 Sep 1975-
Abstract: A data model, called the entity-relationship model, is proposed. This model incorporates some of the important semantic information about the real world. A special diagrammatic technique is introduced as a tool for database design. An example of database design and description using the model and the diagrammatic technique is given. Some implications for data integrity, information retrieval, and data manipulation are discussed.The entity-relationship model can be used as a basis for unification of different views of data: the network model, the relational model, and the entity set model. Semantic ambiguities in these models are analyzed. Possible ways to derive their views of data from the entity-relationship model are presented. more

Topics: Data model (71%), Semi-structured model (70%), Database design (68%) more

3,690 Citations

Open accessJournal ArticleDOI: 10.1016/0022-5096(93)90013-6
Ellen M. Arruda1, Mary C. Boyce1Institutions (1)
Abstract: Aconstitutive model is proposed for the deformation of rubber materials which is shown to represent successfully the response of these materials in uniaxial extension, biaxial extension, uniaxial compression, plane strain compression and pure shear. The developed constitutive relation is based on an eight chain representation of the underlying macromolecular network structure of the rubber and the non-Gaussian behavior of the individual chains in the proposed network. The eight chain model accurately captures the cooperative nature of network deformation while requiring only two material parameters, an initial modulus and a limiting chain extensibility. Since these two parameters are mechanistically linked to the physics of molecular chain orientation involved in the deformation of rubber, the proposed model represents a simple and accurate constitutive model of rubber deformation. The chain extension in this network model reduces to a function of the root-mean-square of the principal applied stretches as a result of effectively sampling eight orientations of principal stretch space. The results of the proposed eight chain model as well as those of several prominent models are compared with experimental data of Treloar (1944, Trans. Faraday Soc. 40, 59) illustrating the superiority, simplicity and predictive ability of the proposed model. Additionally, a new set of experiments which captures the state of deformation dependence of rubber is described and conducted on three rubber materials. The eight chain model is found to model and predict accurately the behavior of the three tested materials further confirming its superiority and effectiveness over earlier models. more

  • FIG. 4. Eight chain rubber elasticity model for (a) undeformed, (b) uniaxial extension and (c) biaxial extension configurations.
    FIG. 4. Eight chain rubber elasticity model for (a) undeformed, (b) uniaxial extension and (c) biaxial extension configurations.
Topics: Yeoh (60%), Neo-Hookean solid (56%), Natural rubber (54%) more

2,280 Citations

Open accessProceedings Article
Bernd Fritzke1Institutions (1)
01 Jan 1994-
Abstract: An incremental network model is introduced which is able to learn the important topological relations in a given set of input vectors by means of a simple Hebb-like learning rule. In contrast to previous approaches like the "neural gas" method of Martinetz and Schulten (1991, 1994), this model has no parameters which change over time and is able to continue learning, adding units and connections, until a performance criterion has been met. Applications of the model include vector quantization, clustering, and interpolation. more

Topics: Neural gas (72%), Learning rule (61%), Time delay neural network (58%) more

1,728 Citations

Open accessDissertation
01 Jan 1998-
Abstract: Control loops that are closed over a communication network get more and more common. A problem with such systems is that the transfer delays will be varying with different characteristics depending on the network hardware and software. The network delays are typically varying due to varying network load, scheduling policies in the network and the nodes, and due to network failures. Two network models of different complexity are studied: Random delays that are independent from transfer to transfer, Random delays with probability distribution functions governed by an underlying Markov chain. The delay models are verified by experimental measurements of network delays. In the thesis it is shown how to analyze stability and expected performance of linear controllers where the network delays are described by one of the two network models above. Methods to evaluate quadratic cost functions are developed. Through the same analysis we find criteria for mean square stability of the closed loop for the different network models. The Linear Quadratic Gaussian (LQG) optimal controller is developed for the two delay models. The derived controller uses knowledge of old time delays. These can be calculated using ``timestamping'' of messages in the network. ``Timestamping'' means that every transfered signal is marked with the time of generation. The receiving node can then calculate how long the transfer delay was by comparing the timestamp with the node's internal clock. (Less) more

Topics: Network delay (67%), Queuing delay (65%), Network model (54%) more

1,195 Citations

Open accessJournal ArticleDOI: 10.1016/S0375-9601(99)00757-4
Mark Newman1, Duncan J. Watts1Institutions (1)
06 Dec 1999-Physics Letters A
Abstract: We study the small-world network model, which mimics the transition between regular-lattice and random-lattice behavior in social networks of increasing size. We contend that the model displays a critical point with a divergent characteristic length as the degree of randomness tends to zero. We propose a real-space renormalization group transformation for the model and demonstrate that the transformation is exact in the limit of large system size. We use this result to calculate the exact value of the single critical exponent for the system, and to derive the scaling form for the average number of `degrees of separation' between two nodes on the network as a function of the three independent variables. We confirm our results by extensive numerical simulation. more

Topics: Critical exponent (56%), Network model (54%), Renormalization group (54%) more

1,090 Citations

No. of papers in the topic in previous years

Top Attributes

Show by:

Topic's top 5 most impactful authors

Jan Treur

18 papers, 97 citations

Guanrong Chen

18 papers, 781 citations

Muriel Medard

6 papers, 110 citations

Yinhe Wang

6 papers, 117 citations

Carolina Osorio

6 papers, 63 citations

Network Information
Related Topics (5)
Artificial neural network

207K papers, 4.5M citations

89% related
Cluster analysis

146.5K papers, 2.9M citations

87% related
Robustness (computer science)

94.7K papers, 1.6M citations

87% related
Probability distribution

40.9K papers, 1.1M citations

87% related
Search algorithm

21.8K papers, 527K citations

86% related