Building Network Distance Maps in Practice

2004 ◽  
pp. 51-66
Author(s):  
Cheng Jin ◽  
Sugih Jamin ◽  
Danny Raz ◽  
Yuval Shavitt
Keyword(s):  
Network Distance

TNDP: Tensor-based Network Distance Prediction with Confidence Intervals

2021 ◽  
pp. 1-1
Author(s):  
Haojun Huang ◽  
Li Li ◽  
Geyong Min ◽  
Wang Miao ◽  
Yingying Zhu ◽  
...  
Keyword(s):  
Confidence Intervals ◽  
Network Distance ◽  
Distance Prediction

Network distance estimation with dynamic landmark triangles

2008 ◽  
Vol 36 (1) ◽  
pp. 433-434
Author(s):  
Zhihua Wen ◽  
Michael Rabinovich
Keyword(s):  
Distance Estimation ◽  
Network Distance

scholarly journals Parallelism in web services: design of parallel XML parser for web services

2019 ◽  
Vol 16 (3) ◽  
pp. 1407
Author(s):  
Rohitkumar Rudrappa Wagdarikar ◽  
Sandhya P
Keyword(s):  
Web Services ◽  
Response Time ◽  
Network Performance ◽  
Heterogeneous Systems ◽  
Data System ◽  
The Internet ◽  
Network Distance ◽  
Consumer Provider ◽  
Internet Connections

<p>A WS provides the communication between heterogeneous systems. While performing this operation, we need to focus on QoS of consumer, provider and registry directory. There will be some parameters like WS selection, prediction and rank these are parameters need to consider while QoS implementation in web services. While performing integration in web services we need to focus on QoS requirements regarding server and network performance. Performance of WS is related to locations i.e the network distance and the Internet connections between consumer and provider. There will be more QoS approach which works on consumers collected QoS data, based on this data system can predict the QoS of WS. Throughput and response time are the QoS of WS. In this paper, we have proposed parallel XML parser, by which we can parse UDDI, WSDL and SOAP XML files parallel by which it will improve the response time and throughput of WS.</p>

Fury Route: Leveraging CDNs to Remotely Measure Network Distance

2018 ◽  
pp. 87-99 ◽  
Author(s):  
Marcel Flores ◽  
Alexander Wenzel ◽  
Kevin Chen ◽  
Aleksandar Kuzmanovic
Keyword(s):  
Network Distance

Context-aware Distance Measures for Dynamic Networks

2022 ◽  
Vol 16 (1) ◽  
pp. 1-34
Author(s):  
Yiji Zhao ◽  
Youfang Lin ◽  
Zhihao Wu ◽  
Yang Wang ◽  
Haomin Wen
Keyword(s):  
Large Scale ◽  
Dynamic Networks ◽  
Distance Measures ◽  
General Definition ◽  
Mathematical Representation ◽  
Network Clustering ◽  
Context Aware ◽  
Network Distance ◽  
Practical Applications ◽  
Spectral Distance

Dynamic networks are widely used in the social, physical, and biological sciences as a concise mathematical representation of the evolving interactions in dynamic complex systems. Measuring distances between network snapshots is important for analyzing and understanding evolution processes of dynamic systems. To the best of our knowledge, however, existing network distance measures are designed for static networks. Therefore, when measuring the distance between any two snapshots in dynamic networks, valuable context structure information existing in other snapshots is ignored. To guide the construction of context-aware distance measures, we propose a context-aware distance paradigm, which introduces context information to enrich the connotation of the general definition of network distance measures. A Context-aware Spectral Distance (CSD) is then given as an instance of the paradigm by constructing a context-aware spectral representation to replace the core component of traditional Spectral Distance (SD). In a node-aligned dynamic network, the context effectively helps CSD gain mainly advantages over SD as follows: (1) CSD is not affected by isospectral problems; (2) CSD satisfies all the requirements of a metric, while SD cannot; and (3) CSD is computationally efficient. In order to process large-scale networks, we develop a kCSD that computes top- k eigenvalues to further reduce the computational complexity of CSD. Although kCSD is a pseudo-metric, it retains most of the advantages of CSD. Experimental results in two practical applications, i.e., event detection and network clustering in dynamic networks, show that our context-aware spectral distance performs better than traditional spectral distance in terms of accuracy, stability, and computational efficiency. In addition, context-aware spectral distance outperforms other baseline methods.

scholarly journals Phylogeography of msp4 genotypes of Anaplasma marginale in beef cattle from the Brazilian Pantanal

2019 ◽  
Vol 28 (3) ◽  
pp. 451-457 ◽  
Author(s):  
Inalda Angélica de Souza Ramos ◽  
Heitor Miraglia Herrera ◽  
Natália Serra Mendes ◽  
Simone de Jesus Fernandes ◽  
João Bosco Vilela Campos ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Conventional Pcr ◽  
Network Distance ◽  
Msp4 Gene ◽  
Distance Analysis ◽  
Network Analyses ◽  
Breeding Farms ◽  
Brazilian Pantanal ◽  
Extensive Breeding

Abstract The msp4 gene of A. marginale is unicodon, stable and mostly homogeneous, being considered as a useful marker for phylogeographic characterization of this bacterium. The objective of this work was to analyze the phylogeography of A. marginale based on the msp4 gene in beef cattle from the Brazilian Pantanal, compared to those found in other regions worldwide. The blood samples investigated were collected from 400 animals (200 cows and 200 calves) reared in five extensive breeding farms in this region. The results indicated that of the evaluated samples, 56.75% (227/400) were positive for A. marginale based on the msp1β gene by quantitatitve PCR (qPCR), while 8.37% (19/227) were positive for the msp4 gene in the conventional PCR. In the Network distance analysis, 14 sequences from the Brazilian Pantanal were grouped into a single group with those from Thailand, India, Spain, Colombia, Parana (Brazil), Mexico, Portugal, Argentina, China, Venezuela, Australia, Italy and Minas Gerais (Brazil). Among 68 sequences from Brazil and the world, 15 genotypes were present while genotype number one (#1) was the most distributed worldwide. Both Splitstree and network analyses showed that the A. marginale msp4 sequences detected in beef cattle from the Brazilian Pantanal showed low polymorphism, with the formation of one genogroup phylogenetically related to those found in ruminants from South and Central America, Europe, and Asia.

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