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Network topology

About: Network topology is a research topic. Over the lifetime, 52259 publications have been published within this topic receiving 1006627 citations.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors present a new, comprehensive, system-wide approach to identify critical links and evaluate network performance, which considers network flows, link capacity and network topology.

458 citations

Dissertation
01 Jan 1996
TL;DR: This survey paper first describes the general issues in constructive algorithms, with special emphasis on the search strategy, then presents a taxonomy, based on the differences in the state transition mapping, the training algorithm, and the network architecture.
Abstract: In recent years, multi-layer feedforward neural networks have been popularly used for pattern classification, function approximation and regression problems. Methods using standard back-propagation learning algorithm perform gradient descent only in the weight space of a network with fixed topology. Recently, various researchers have investigated different approaches that alter the network topology as learning proceeds. In this thesis, I concentrate on constructive algorithms for structure learning in feedforward neural networks for regression problems. The basic idea of constructive algorithms is to start with a small network, then add hidden units and weights incrementally until a satisfactory solution is found. There are hurdles that constructive algorithms have to overcome, including: (1) How to train the new hidden unit? (2) Whether the constructive algorithms can produce a neural network function that is as close to an arbitrary target function as desired? (3) How to control the complexity of the new hidden unit? To address these issues, I develop a number of objective functions for training new hidden units. The theoretical convergence properties of a number of constructive algorithms, in which hidden units are added one by one in a greedy manner, are also examined. Moreover, I study how the integration of Bayesian regularization and constructive algorithms can lead to improved network performance. The approach is promising in that the regularization parameters can be automatically controlled in a disciplined manner without requiring manual setting.* ftn*Originally published in DAI Vol. 58, No. 11. Reprinted here with corrected author name.

457 citations

Proceedings ArticleDOI
25 Jul 2011
TL;DR: Experiments are presented on a real bibliographic network, the DBLP network, which show that metapath-based heterogeneousTopological features can generate more accurate prediction results as compared to homogeneous topological features.
Abstract: The problem of predicting links or interactions between objects in a network, is an important task in network analysis. Along this line, link prediction between co-authors in a co-author network is a frequently studied problem. In most of these studies, authors are considered in a homogeneous network, \i.e., only one type of objects(author type) and one type of links (co-authorship) exist in the network. However, in a real bibliographic network, there are multiple types of objects (\e.g., venues, topics, papers) and multiple types of links among these objects. In this paper, we study the problem of co-author relationship prediction in the heterogeneous bibliographic network, and a new methodology called\emph{Path Predict}, \i.e., meta path-based relationship prediction model, is proposed to solve this problem. First, meta path-based topological features are systematically extracted from the network. Then, a supervised model is used to learn the best weights associated with different topological features in deciding the co-author relationships. We present experiments on a real bibliographic network, the DBLP network, which show that metapath-based heterogeneous topological features can generate more accurate prediction results as compared to homogeneous topological features. In addition, the level of significance of each topological feature can be learned from the model, which is helpful in understanding the mechanism behind the relationship building.

456 citations

Journal ArticleDOI
TL;DR: The ASCENT algorithm is motivated and described and it is shown that the system achieves linear increase in energy savings as a function of the density and the convergence time required in case of node failures while still providing adequate connectivity.
Abstract: Advances in microsensor and radio technology enable small but smart sensors to be deployed for a wide range of environmental monitoring applications. The low-per node cost allows these wireless networks of sensors and actuators to be densely distributed. The nodes in these dense networks coordinate to perform the distributed sensing and actuation tasks. Moreover, as described in this paper, the nodes can also coordinate to exploit the redundancy provided by high density so as to extend overall system lifetime. The large number of nodes deployed in this systems preclude manual configuration, and the environmental dynamics precludes design-time preconfiguration. Therefore, nodes have to self-configure to establish a topology that provides communication under stringent energy constraints. ASCENT builds on the notion that, as density increases, only a subset of the nodes is necessary to establish a routing forwarding backbone. In ASCENT, each node assesses its connectivity and adapts its participation in the multihop network topology based on the measured operating region. This paper motivates and describes the ASCENT algorithm and presents analysis, simulation, and experimental measurements. We show that the system achieves linear increase in energy savings as a function of the density and the convergence time required in case of node failures while still providing adequate connectivity.

452 citations

Journal ArticleDOI
Junyan Yu1, Long Wang1
TL;DR: This work introduces double-tree-form transformations under which dynamic equations of agents are transformed into reduced-order systems and obtains some analysis results for group consensus problems in networks of dynamic agents.

449 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20231,292
20223,051
20212,286
20202,746
20192,992
20183,259