Scalable Algorithms For Data And Network Analysis
DOWNLOAD
Download Scalable Algorithms For Data And Network Analysis PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Scalable Algorithms For Data And Network Analysis book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Scalable Algorithms For Data And Network Analysis
DOWNLOAD
Author : Shang-Hua Teng
language : en
Publisher:
Release Date : 2016
Scalable Algorithms For Data And Network Analysis written by Shang-Hua Teng and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Big data categories.
In the age of Big Data, efficient algorithms are now in higher demand more than ever before. While Big Data takes us into the asymptotic world envisioned by our pioneers, it also challenges the classical notion of efficient algorithms: Algorithms that used to be considered efficient, according to polynomial-time characterization, may no longer be adequate for solving today's problems. It is not just desirable, but essential, that efficient algorithms should be scalable. In other words, their complexity should be nearly linear or sub-linear with respect to the problem size. Thus, scalability, not just polynomial-time computability, should be elevated as the central complexity notion for characterizing efficient computation. In this tutorial, I will survey a family of algorithmic techniques for the design of provably-good scalable algorithms. These techniques include local network exploration, advanced sampling, sparsification, and geometric partitioning. They also include spectral graph-theoretical methods, such as those used for computing electrical flows and sampling from Gaussian Markov random fields. These methods exemplify the fusion of combinatorial, numerical, and statistical thinking in network analysis. I will illustrate the use of these techniques by a few basic problems that are fundamental in network analysis, particularly for the identification of significant nodes and coherent clusters/communities in social and information networks. I also take this opportunity to discuss some frameworks beyond graph-theoretical models for studying conceptual questions to understand multifaceted network data that arise in social influence, network dynamics, and Internet economics.
Scalable Algorithms For The Analysis Of Massive Networks
DOWNLOAD
Author : Eugenio Angriman
language : en
Publisher:
Release Date : 2021*
Scalable Algorithms For The Analysis Of Massive Networks written by Eugenio Angriman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021* with categories.
On The Analysis Of Complex Networks
DOWNLOAD
Author : Feizi-Khankandi Feizi
language : en
Publisher:
Release Date : 2016
On The Analysis Of Complex Networks written by Feizi-Khankandi Feizi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
Network models provide a unifying framework for understanding dependencies among variables in data-driven and engineering sciences. Networks can be used to reveal underlying data structures, infer functional modules, and facilitate experiment design. In practice, however, size, uncertainty and complexity of the underlying associations render these applications challenging. In this thesis, we illustrate the use of spectral, combinatorial, and statistical inference techniques in several network science problems. In Chapters 2-4, we consider network inference challenges. In Chapter 2, we introduce Network Maximal Correlation (NMC) as a multivariate measure of nonlinear association suitable for evaluation on large datasets. We characterize a solution of the NMC optimization using geometric properties of Hilbert spaces for finite discrete and jointly Gaussian random variables. We illustrate an application of NMC and multiple MC in inference of graphical models for bijective, possibly non-monotone, functions of jointly Gaussian variables. As a demonstration of NMC's utility, we infer nonlinear gene association networks and modules in cancer datasets and validate them using survival times of patients. In Chapter 3, we develop a network integration framework to infer gene regulatory networks in human and model organisms fly and worm using diverse and high-throughput datasets. Inferred regulatory interactions have significant overlap with known edges, indicating the robustness and accuracy of the proposed network inference framework. In Chapter 4, we formulate the transitive noise problem in networks as the inverse of matrix transitive closure and introduce an algorithm to solve it efficiently. We demonstrate the effectiveness of our approach in several applications such as regulatory network inference, protein contact map inference and strong collaboration tie inference. In Chapters 5-8, we consider network analysis challenges. In Chapter 5, we consider the problem of network alignment where the goal is to find a bijective mapping between nodes of two networks to maximize their overlapping edges while minimizing mismatches. This problem is essential in comparative analysis across large datasets and networks. To solve this combinatorial problem, we present a new scalable spectral algorithm which creates an eigenvector relaxation for the underlying optimization. We prove the optimality of the method under certain technical conditions, and show its effectiveness over various synthetic networks as well as in comparative analysis of gene regulatory networks across human, fly and worm species. In Chapter 6, we consider the source inference problem where the goal is to identify the source(s) of propagated signals across biological, social and engineered networks. To solve this problem, we propose a computationally tractable general method based on a path-based network diffusion kernel. We prove mean-field optimality of this method for different scenarios and show its effectiveness over several synthetic networks as well as in identifying sources in a Digg social news network. In Chapter 7, we consider the problem of learning low dimensional structures (such as clusters) in large networks. Here we introduce logistic Random Dot Product Graphs (RDPGs) as a new class of networks which includes most stochastic block models as well as other low dimensional structures. Using this model, we propose a scalable spectral method that solves the maximum likelihood inference problem asymptotically exactly. This leads to a new scalable spectral network clustering algorithm that is robust under different clustering setups. In Chapter 8, we consider the biclustering problem, the analog of clustering on bipartite graphs. This problem has several applications such as inference of co-regulated genes, document classification, and so on. Here we propose an algorithm based on message-passing that closely approximates a general likelihood function and excels at resolving the overlaps between biclusters. In Chapters 9-12, we consider design challenges of systems and algorithms for engineering networks such as communication networks. In Chapters 9-10, we create a connection between compressive sensing and traditional information theoretic techniques in source, channel and network coding and propose a joint coding scheme over wireless networks based on random projection and restricted eigenvalue principles. Moreover, we characterize fundamental results on the trade-off between the communication rate and the decoding complexity. In Chapters 11-12, we propose an adaptive nonuniform sampling framework, in which time increments between samples are determined as a function of the most recent increments and sample values, obviating the need to track time stamps. We analyze the performance of the proposed method for different stochastic and deterministic signal models and show its effectiveness to enhance measurements of heart ECG signals.
Scalable Algorithms For Data And Network Analysis
DOWNLOAD
Author : Shang-Hua Teng
language : en
Publisher:
Release Date : 2016-05-04
Scalable Algorithms For Data And Network Analysis written by Shang-Hua Teng and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-05-04 with Computers categories.
In the age of Big Data, efficient algorithms are in high demand. It is also essential that efficient algorithms should be scalable. This book surveys a family of algorithmic techniques for the design of scalable algorithms. These techniques include local network exploration, advanced sampling, sparsification, and geometric partitioning.
Scalable Fuzzy Algorithms For Data Management And Analysis Methods And Design
DOWNLOAD
Author : Laurent, Anne
language : en
Publisher: IGI Global
Release Date : 2009-10-31
Scalable Fuzzy Algorithms For Data Management And Analysis Methods And Design written by Laurent, Anne and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-10-31 with Computers categories.
"This book presents up-to-date techniques for addressing data management problems with logic and memory use"--Provided by publisher.
Algorithms For Big Data
DOWNLOAD
Author : Hannah Bast
language : en
Publisher: Springer Nature
Release Date : 2022
Algorithms For Big Data written by Hannah Bast and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Algorithms categories.
This open access book surveys the progress in addressing selected challenges related to the growth of big data in combination with increasingly complicated hardware. It emerged from a research program established by the German Research Foundation (DFG) as priority program SPP 1736 on Algorithmics for Big Data where researchers from theoretical computer science worked together with application experts in order to tackle problems in domains such as networking, genomics research, and information retrieval. Such domains are unthinkable without substantial hardware and software support, and these systems acquire, process, exchange, and store data at an exponential rate. The chapters of this volume summarize the results of projects realized within the program and survey-related work. This is an open access book.
Distributed Graph Analytics
DOWNLOAD
Author : Unnikrishnan Cheramangalath
language : en
Publisher: Springer Nature
Release Date : 2020-04-17
Distributed Graph Analytics written by Unnikrishnan Cheramangalath and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-17 with Computers categories.
This book brings together two important trends: graph algorithms and high-performance computing. Efficient and scalable execution of graph processing applications in data or network analysis requires innovations at multiple levels: algorithms, associated data structures, their implementation and tuning to a particular hardware. Further, programming languages and the associated compilers play a crucial role when it comes to automating efficient code generation for various architectures. This book discusses the essentials of all these aspects. The book is divided into three parts: programming, languages, and their compilation. The first part examines the manual parallelization of graph algorithms, revealing various parallelization patterns encountered, especially when dealing with graphs. The second part uses these patterns to provide language constructs that allow a graph algorithm to be specified. Programmers can work with these language constructs without worrying about their implementation, which is the focus of the third part. Implementation is handled by a compiler, which can specialize code generation for a backend device. The book also includes suggestive results on different platforms, which illustrate and justify the theory and practice covered. Together, the three parts provide the essential ingredients for creating a high-performance graph application. The book ends with a section on future directions, which offers several pointers to promising topics for future research. This book is intended for new researchers as well as graduate and advanced undergraduate students. Most of the chapters can be read independently by those familiar with the basics of parallel programming and graph algorithms. However, to make the material more accessible, the book includes a brief background on elementary graph algorithms, parallel computing and GPUs. Moreover it presents a case study using Falcon, a domain-specific language for graph algorithms, to illustrate the concepts.
Computing And Combinatorics
DOWNLOAD
Author : Yixin Cao
language : en
Publisher: Springer
Release Date : 2017-07-25
Computing And Combinatorics written by Yixin Cao and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-25 with Computers categories.
This book constitutes the refereed proceedings of the 23rd International Conference on Computing and Combinatorics, COCOON 2017, held in Hiong Kong, China, in August 2017. The 56 full papers papers presented in this book were carefully reviewed and selected from 119 submissions. The papers cover various topics, including algorithms and data structures, complexity theory and computability, algorithmic game theory, computational learning theory, cryptography, computationalbiology, computational geometry and number theory, graph theory, and parallel and distributed computing.
Big Data Analysis New Algorithms For A New Society
DOWNLOAD
Author : Nathalie Japkowicz
language : en
Publisher: Springer
Release Date : 2015-12-16
Big Data Analysis New Algorithms For A New Society written by Nathalie Japkowicz and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-16 with Technology & Engineering categories.
This edited volume is devoted to Big Data Analysis from a Machine Learning standpoint as presented by some of the most eminent researchers in this area. It demonstrates that Big Data Analysis opens up new research problems which were either never considered before, or were only considered within a limited range. In addition to providing methodological discussions on the principles of mining Big Data and the difference between traditional statistical data analysis and newer computing frameworks, this book presents recently developed algorithms affecting such areas as business, financial forecasting, human mobility, the Internet of Things, information networks, bioinformatics, medical systems and life science. It explores, through a number of specific examples, how the study of Big Data Analysis has evolved and how it has started and will most likely continue to affect society. While the benefits brought upon by Big Data Analysis are underlined, the book also discusses some of the warnings that have been issued concerning the potential dangers of Big Data Analysis along with its pitfalls and challenges.
Scalable Data Analytics For Ensemble Learning
DOWNLOAD
Author : Suhas R. Aithal
language : en
Publisher:
Release Date : 2013
Scalable Data Analytics For Ensemble Learning written by Suhas R. Aithal and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Computer science categories.
Data Anlaytic techniques have enhanced human ability to solve a lot of data related problems. It has opened a window through which an analyst can devise techniques to solve a given problem by just looking at the related data alone. Such techniques may not be directly visible to the programmer. Machine learning in short is programming computers to generate such algorithm to solve a given problem using example data or past experience. Numerous models or functions can be developed which fits a distribution of data points. The magnitude and breadth of this data plays a major role in determining which model fits best. In the current world, data is growing at an alarming rate both in terms of size and the information it conveys. Analytics on large-scale data requires very high execution time with limited resources. Implementing such techniques to generate different models on a single machine becomes tedious and time consuming. Implementing the same on a distributed network is a possible solution which is highly interesting and challenging. Data Analytic Algorithms, to create these models, should be carefully chosen on the basis of reliability and data integrity and at the same time should be easily distributable. The range of algorithms should be wide enough to incorporate the same analytics on all the data with required performance. An analysis of various metrics such as measure of scalability, accuracy, execution time helps classify individual algorithm's impact on the required performance. The above metrics are highly dependent on the data, type of algorithm, and type of platform under consideration. This work proposes a unique system for Scalable Data Analytics and gives an analysis of its scalability. It also shows how this system implements a unique way of ensemble learning in useful time without compromising on accuracy. Finally it demonstrates scalability on two levels 1. Scalability at an individual algorithm level 2. Scalability at implementing ensemble learning for different use cases.