scholarly journals Developing a LTE Localization Framework using Real Network Data towards RAN Optimization through Context Knowledge

2020 ◽  
Author(s):  
R. Borralho ◽  
D. Duarte ◽  
A. Quddus ◽  
P. Vieira
Keyword(s):  
Network Data ◽  
Real Network ◽  
Context Knowledge ◽  
Real Network Data

scholarly journals Hydra: a method for strain-minimizing hyperbolic embedding of network- and distance-based data

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Martin Keller-Ressel ◽  
Stephanie Nargang
Keyword(s):  
Computation Time ◽  
Feature Space ◽  
Hyperbolic Distance ◽  
Network Data ◽  
Data Points ◽  
Low Dimensional ◽  
Hyperbolic Embedding ◽  
Embedding Methods ◽  
Real Network Data

Abstract We introduce hydra (hyperbolic distance recovery and approximation), a new method for embedding network- or distance-based data into hyperbolic space. We show mathematically that hydra satisfies a certain optimality guarantee: it minimizes the ‘hyperbolic strain’ between original and embedded data points. Moreover, it is able to recover points exactly, when they are contained in a low-dimensional hyperbolic subspace of the feature space. Testing on real network data we show that the embedding quality of hydra is competitive with existing hyperbolic embedding methods, but achieved at substantially shorter computation time. An extended method, termed hydra+, typically outperforms existing methods in both computation time and embedding quality.

A Group Testing-Based Distributed Network Traffic Monitoring Approach for Smart Grid

2013 ◽  
Vol 791-793 ◽  
pp. 892-896
Author(s):  
Hong Hao Zhao ◽  
Fan Bo Meng ◽  
Qing Qi Zhao ◽  
Wei Zhe Ma ◽  
Zhi Chao Lin ◽  
...  
Keyword(s):  
Communication Network ◽  
Smart Grid ◽  
Network Traffic ◽  
Group Testing ◽  
Traffic Monitoring ◽  
Network Data ◽  
Distributed Network ◽  
Recovery Algorithm ◽  
Network Traffic Monitoring ◽  
Real Network Data

In this paper, we address the problem of real-time network traffic monitoring in the communication network of smart grid. And we propose an effective distributed network traffic monitoring approach. In our algorithm, instead of measuring all the origin-destination pairs, we just need to measure partial origin-destination pairs that flows our communication network. From the measured origin-destination pairs, we can obtain all the origin-destination pairs via our recovery algorithm. Finally, we validate the properties of our method by real network data.

Classes of preferential attachment and triangle preferential attachment models with power-law spectra

2019 ◽  
Vol 8 (4) ◽  
Author(s):  
Nicole Eikmeier ◽  
David F Gleich
Keyword(s):  
Power Law ◽  
Real World ◽  
Preferential Attachment ◽  
Power Laws ◽  
Network Data ◽  
Degree Sequences ◽  
Graph Models ◽  
Attachment Model ◽  
Power Law Distributions ◽  
Real Network Data

Abstract Preferential attachment (PA) models are a common class of graph models which have been used to explain why power-law distributions appear in the degree sequences of real network data. Among other properties of real-world networks, they commonly have non-trivial clustering coefficients due to an abundance of triangles as well as power laws in the eigenvalue spectra. Although there are triangle PA models and eigenvalue power laws in specific PA constructions, there are no results that existing constructions have both. In this article, we present a specific Triangle Generalized Preferential Attachment Model that, by construction, has non-trivial clustering. We further prove that this model has a power law in both the degree distribution and eigenvalue spectra.

Abstract #1249: Novel Use of Physician Social Network Data to Map the Gap in the Endocrinology Workforce

2015 ◽  
Vol 21 ◽  
pp. 301
Author(s):  
Armand Krikorian ◽  
Lily Peng ◽  
Zubair Ilyas ◽  
Joumana Chaiban
Keyword(s):  
Social Network ◽  
Network Data ◽  
Social Network Data

The Multilevel p2 Model

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 42-47 ◽  
Author(s):  
Bonne J. H. Zijlstra ◽  
Marijtje A. J. van Duijn ◽  
Tom A. B. Snijders
Keyword(s):  
Social Network ◽  
Network Data ◽  
Mcmc Algorithm ◽  
Independent Observation ◽  
Single Observation ◽  
Multiple Networks ◽  
Social Network Data ◽  
Multiple Observations ◽  
Practical Support ◽  
The Social

The p 2 model is a random effects model with covariates for the analysis of binary directed social network data coming from a single observation of a social network. Here, a multilevel variant of the p 2 model is proposed for the case of multiple observations of social networks, for example, in a sample of schools. The multilevel p 2 model defines an identical p 2 model for each independent observation of the social network, where parameters are allowed to vary across the multiple networks. The multilevel p 2 model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) algorithm that was implemented in free software for the statistical analysis of complete social network data, called StOCNET. The new model is illustrated with a study on the received practical support by Dutch high school pupils of different ethnic backgrounds.

Collection of Ego-Centered Network Data with Computer-Assisted Interviews

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 7-15 ◽  
Author(s):  
Joachim Gerich ◽  
Roland Lehner
Keyword(s):  
Survey Methods ◽  
A Priori ◽  
Dynamic Structure ◽  
Computer Assisted ◽  
Network Data ◽  
Great Effort ◽  
Self Administration ◽  
Face To Face ◽  
Full Network

Although ego-centered network data provide information that is limited in various ways as compared with full network data, an ego-centered design can be used without the need for a priori and researcher-defined network borders. Moreover, ego-centered network data can be obtained with traditional survey methods. However, due to the dynamic structure of the questionnaires involved, a great effort is required on the part of either respondents (with self-administration) or interviewers (with face-to-face interviews). As an alternative, we will show the advantages of using CASI (computer-assisted self-administered interview) methods for the collection of ego-centered network data as applied in a study on the role of social networks in substance use among college students.

Positional Estimation Within a Latent Space Model for Networks

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 24-33 ◽  
Author(s):  
Susan Shortreed ◽  
Mark S. Handcock ◽  
Peter Hoff
Keyword(s):  
Random Effects ◽  
Network Data ◽  
Random Effects Models ◽  
Space Model ◽  
Network Analyses ◽  
Recent Advances ◽  
Latent Space ◽  
Latent Space Model ◽  
Modeling Data

Recent advances in latent space and related random effects models hold much promise for representing network data. The inherent dependency between ties in a network makes modeling data of this type difficult. In this article we consider a recently developed latent space model that is particularly appropriate for the visualization of networks. We suggest a new estimator of the latent positions and perform two network analyses, comparing four alternative estimators. We demonstrate a method of checking the validity of the positional estimates. These estimators are implemented via a package in the freeware statistical language R. The package allows researchers to efficiently fit the latent space model to data and to visualize the results.

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