Download or read book Scalable Algorithms for the Analysis of Massive Networks written by Eugenio Angriman and published by . This book was released on 2021* with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Scalable Algorithms for Data and Network Analysis written by Shang-Hua Teng and published by . This book was released on 2016-05-04 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Scalable Community Detection in Massive Networks Using Aggregated Relational Data written by Timothy Jones and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Our inference method converges faster than existing methods by leveraging nodal information that often accompany real world networks. Conditioning on this extra information leads to a model that admits a parallel variational inference algorithm. We apply our method to a citation network with over three million nodes and 25 million edges. Our method converges faster than existing posterior inference algorithms for the MMSB and recovers parameters better on simulated networks generated according to the MMSB.

Download or read book Scalabale Algorithms for Updating Large Scale Dynamic Networks written by Sriram Srinivasan and published by . This book was released on 2020 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: The growth of social media and data in various domains increased the interest in analyzing network algorithms. The networks are highly unstructured and exhibit poor locality, which has been a challenge for developing scalable parallel algorithms. The state-of-the-art network algorithms such as Prim's algorithm for Minimum Spanning Tree, Dijkstra's algorithm for Single Source Shortest Path, Google's Page Rank algorithm, and iSpan algorithm for detecting strongly connected components are designed and optimized for static networks. For the networks that change with time, i.e. the dynamic networks (such as social networks, biological networks, or temporal networks) the above-mentioned approaches can only be utilized if they are computed from scratch each time. Performing a computation from scratch for a significant amount of changes is not only computationally expensive, however, increases the memory footprint and the execution time. In the case of dynamic networks, developing scalable parallel algorithms is very challenging and there has been a very limited amount of research work that has been performed when compared to developing parallel scalable algorithms for static networks. To address the above challenges, this Ph. D. dissertation proposes a new high performance, scalable, portable, open-source software package, and an efficient network data structure to update the dynamic networks on the fly. This approach is different from the naive approach which is the re-computation from scratch and is scalable for random, small-world, scale-free, real-world, and synthetic networks. The software package currently is implemented on a shared memory system and GPU which updates network properties such as Connected Components (CC), Minimum Spanning Tree (MST), Single Source Shortest Path (SSSP), Page Rank (PR), and Strongly Connected Components(SCC). The key attributes of the software are faster insertions and deletions. Additionally, the software takes less time and memory for updating the networks when compared to the state of the art Galois(CPU), and Gunrock (GPU). The GPU implementation processes over 50 million updates for updating SSSP on a real-world network in under 300 seconds. This dissertation also provides a novel shared memory implementation of detecting, overlapping, and non-overlapping communities on static networks using Permanence. Detecting communities on large scale networks is a fundamental operation in various domains. Detecting correct communities is a challenging problem due to the limitations of the metric such as the state-of-the-art metric modularity since it suffers from the resolution limit. This dissertation is the first attempt to implement shared memory overlapping and non-overlapping communities using permanence. The key attributes of this implementation are the accuracy of the communities when compared to the ground truth and achieve speed up to 10× when compared to its sequential implementation. The dissertation concludes with a summarization of the contributions and their improvement in large-scale network analytics and a discussion about future work in this field.

Download or read book Large Data Algorithmics written by Bahman Bahmani and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, we will explore the algorithmic aspect of large data applications on distributed frameworks. In the distributed batched processing setting, I will present highly scalable algorithms for the densest subgraph detection primitive in massive networks, as well as an efficient scalable algorithm called k-means.

Download or read book On the Analysis of Complex Networks written by Feizi-Khankandi Feizi and published by . This book was released on 2016 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Big Data written by James Warren and published by Simon and Schuster. This book was released on 2015-04-29 with total page 481 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Big Data teaches you to build big data systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. It describes a scalable, easy-to-understand approach to big data systems that can be built and run by a small team. Following a realistic example, this book guides readers through the theory of big data systems, how to implement them in practice, and how to deploy and operate them once they're built. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Book Web-scale applications like social networks, real-time analytics, or e-commerce sites deal with a lot of data, whose volume and velocity exceed the limits of traditional database systems. These applications require architectures built around clusters of machines to store and process data of any size, or speed. Fortunately, scale and simplicity are not mutually exclusive. Big Data teaches you to build big data systems using an architecture designed specifically to capture and analyze web-scale data. This book presents the Lambda Architecture, a scalable, easy-to-understand approach that can be built and run by a small team. You'll explore the theory of big data systems and how to implement them in practice. In addition to discovering a general framework for processing big data, you'll learn specific technologies like Hadoop, Storm, and NoSQL databases. This book requires no previous exposure to large-scale data analysis or NoSQL tools. Familiarity with traditional databases is helpful. What's Inside Introduction to big data systems Real-time processing of web-scale data Tools like Hadoop, Cassandra, and Storm Extensions to traditional database skills About the Authors Nathan Marz is the creator of Apache Storm and the originator of the Lambda Architecture for big data systems. James Warren is an analytics architect with a background in machine learning and scientific computing. Table of Contents A new paradigm for Big Data PART 1 BATCH LAYER Data model for Big Data Data model for Big Data: Illustration Data storage on the batch layer Data storage on the batch layer: Illustration Batch layer Batch layer: Illustration An example batch layer: Architecture and algorithms An example batch layer: Implementation PART 2 SERVING LAYER Serving layer Serving layer: Illustration PART 3 SPEED LAYER Realtime views Realtime views: Illustration Queuing and stream processing Queuing and stream processing: Illustration Micro-batch stream processing Micro-batch stream processing: Illustration Lambda Architecture in depth

Download or read book Algorithms for Big Data written by Hannah Bast and published by Springer Nature. This book was released on 2022 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Scalable Algorithms for Misinformation Prevention in Social Networks written by Michael Simpson and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis investigates several problems in social network analysis on misinformation prevention with an emphasis on finding solutions that can scale to massive online networks. In particular, it considers two problem formulations related to the spread of misinformation in a network that cover the elimination of existing misinformation and the prevention of future dissemination of misinformation. Additionally, a comprehensive comparison of several algorithms for the feedback arc set (FAS) problem is presented in order to identify an approach that is both scalable and computes a lightweight solution. The feedback arc set problem is of particular interest since several notable problems in social network analysis, including the elimination of existing misinformation, crucially rely on computing a small FAS as a preliminary. The elimination of existing misinformation is modelled as a graph searching game. The problem can be summarized as constructing a search strategy that will leave the graph clear of any misinformation at the end of the searching process in as few steps as possible. Despite the problem being NP-hard, even on directed acyclic graphs, this thesis presents an efficient approximation algorithm and provides new experimental results that compares the performance of the approximation algorithm to the lower bound on several large online networks. In particular, new scalability goals are achieved through careful algorithmic engineering and a highly optimized pre-processing step. The minimum feedback arc set problem is an NP-hard problem on graphs that seeks a minimum set of arcs which, when removed from the graph, leave it acyclic. A comprehensive comparison of several approximation algorithms for computing a minimum feedback arc set is presented with the goal of comparing the quality of the solutions and the running times. Additionally, careful algorithmic engineering is applied for multiple algorithms in order to improve their scalability. In particular, two approaches that are optimized (one greedy and one randomized) result in simultaneously strong performance for both feedback arc set size and running time. The experiments compare the performance of a wide range of algorithms on a broad selection of large online networks and reveal that the optimized greedy and randomized implementations outperform the other approaches by simultaneously computing a feedback arc set of competitive size and scaling to web-scale graphs with billions of vertices and tens of billions of arcs. Finally, the algorithms considered are extended to the probabilistic case in which arcs are realized with some fixed probability and a detailed experimental comparison is provided. \sloppy Finally, the problem of preventing the spread of misinformation propagating through a social network is considered. In this problem, a ``bad'' campaign starts propagating from a set of seed nodes in the network and the notion of a limiting (or ``good'') campaign is used to counteract the effect of misinformation. The goal is to identify a set of $k$ users that need to be convinced to adopt the limiting campaign so as to minimize the number of people that adopt the ``bad'' campaign at the end of both propagation processes. \emph{RPS} (Reverse Prevention Sampling), an algorithm that provides a scalable solution to the misinformation prevention problem, is presented. The theoretical analysis shows that \emph{RPS} runs in $O((k + l)(n + m)(\frac{1}{1 - \gamma}) \log n / \epsilon^2 )$ expected time and returns a $(1 - 1/e - \epsilon)$-approximate solution with at least $1 - n^{-l}$ probability (where $\gamma$ is a typically small network parameter). The time complexity of \emph{RPS} substantially improves upon the previously best-known algorithms that run in time $\Omega(m n k \cdot POLY(\epsilon^{-1}))$. Additionally, an experimental evaluation of \emph{RPS} on large datasets is presented where it is shown that \emph{RPS} outperforms the state-of-the-art solution by several orders of magnitude in terms of running time. This demonstrates that misinformation prevention can be made practical while still offering strong theoretical guarantees.

Download or read book 2014 5th Workshop on Latest Advances in Scalable Algorithms for Large Scale Systems ScalA 2014 written by and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Scalable Algorithms for Contact Problems written by Zdeněk Dostál and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a comprehensive treatment of recently developed scalable algorithms for solving multibody contact problems of linear elasticity. The brand-new feature of these algorithms is their theoretically supported numerical scalability (i.e., asymptotically linear complexity) and parallel scalability demonstrated in solving problems discretized by billions of degrees of freedom. The theory covers solving multibody frictionless contact problems, contact problems with possibly orthotropic Tresca's friction, and transient contact problems. In addition, it also covers BEM discretization, treating jumping coefficients, floating bodies, mortar non-penetration conditions, etc. This second edition includes updated content, including a new chapter on hybrid domain decomposition methods for huge contact problems. Furthermore, new sections describe the latest algorithm improvements, e.g., the fast reconstruction of displacements, the adaptive reorthogonalization of dual constraints, and an updated chapter on parallel implementation. Several chapters are extended to give an independent exposition of classical bounds on the spectrum of mass and dual stiffness matrices, a benchmark for Coulomb orthotropic friction, details of discretization, etc. The exposition is divided into four parts, the first of which reviews auxiliary linear algebra, optimization, and analysis. The most important algorithms and optimality results are presented in the third chapter. The presentation includes continuous formulation, discretization, domain decomposition, optimality results, and numerical experiments. The final part contains extensions to contact shape optimization, plasticity, and HPC implementation. Graduate students and researchers in mechanical engineering, computational engineering, and applied mathematics will find this book of great value and interest.

Download or read book Transactions on Large Scale Data and Knowledge Centered Systems XLII written by Abdelkader Hameurlain and published by Springer Nature. This book was released on 2019-10-17 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. Current decentralized systems still focus on data and knowledge as their main resource. Feasibility of these systems relies basically on P2P (peer-to-peer) techniques and the support of agent systems with scaling and decentralized control. Synergy between grids, P2P systems, and agent technologies is the key to data- and knowledge-centered systems in large-scale environments. This, the 42nd issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, consists of five revised selected regular papers, presenting the following topics: Privacy-Preserving Top-k Query Processing in Distributed Systems; Trust Factors and Insider Threats in Permissioned Distributed Ledgers: An Analytical Study and Evaluation of Popular DLT Frameworks; Polystore and Tensor Data Model for Logical Data Independence and Impedance Mismatch in Big Data Analytics; A General Framework for Multiple Choice Question Answering Based on Mutual Information and Reinforced Co-occurrence; Rejig: A Scalable Online Algorithm for Cache Server Configuration Changes.

Download or read book Large scale Network written by Tossaporn Saengja and published by . This book was released on 2020 with total page 53 pages. Available in PDF, EPUB and Kindle. Book excerpt: The amount of available data is predicted to be more than thousands of gigabytes per human by 2020, and current technologies are connecting people together. Data on observed actions become more available which are able to give insights on the underlying connections between individuals. However, the growing size of data presents challenges for existing machine learning methods and visualization platform. In this thesis, I focus on two problems. First, I extend an existing network learning method to large-scale networks with alternating direction method of multipliers. Testing the method with synthetic datasets, I show that the algorithm achieves similar performance with less computation time. Second, I build a tool for large-scale network data exploration. The tool is tested on several large-scale real-world datasets to illustrate its benefits.

Download or read book Efficient Algorithms in Emerging Large scale Networks written by Lili Cao and published by . This book was released on 2011 with total page 584 pages. Available in PDF, EPUB and Kindle. Book excerpt: Given these significant challenges in real-time graph computation, conventional solutions are no longer suitable or feasible to accomplish those tasks. We must design a new class of highly scalable algorithms that adapt to continual network dynamics.

Download or read book Resource Management for Big Data Platforms written by Florin Pop and published by Springer. This book was released on 2016-10-27 with total page 509 pages. Available in PDF, EPUB and Kindle. Book excerpt: Serving as a flagship driver towards advance research in the area of Big Data platforms and applications, this book provides a platform for the dissemination of advanced topics of theory, research efforts and analysis, and implementation oriented on methods, techniques and performance evaluation. In 23 chapters, several important formulations of the architecture design, optimization techniques, advanced analytics methods, biological, medical and social media applications are presented. These chapters discuss the research of members from the ICT COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications (cHiPSet). This volume is ideal as a reference for students, researchers and industry practitioners working in or interested in joining interdisciplinary works in the areas of intelligent decision systems using emergent distributed computing paradigms. It will also allow newcomers to grasp the key concerns and their potential solutions.

Download or read book Proceedings of the Workshop on Latest Advances in Scalable Algorithms for Large Scale Systems written by William D. Gropp and published by . This book was released on 2013-11-17 with total page 46 pages. Available in PDF, EPUB and Kindle. Book excerpt: SC13: International Conference for High Performance Computing, Networking, Storage and Analysis Nov 17, 2013-Nov 21, 2013 Denver, USA. You can view more information about this proceeding and all of ACM�s other published conference proceedings from the ACM Digital Library: http://www.acm.org/dl.

Download or read book Proceedings of the 6th Workshop on Latest Advances in Scalable Algorithms for Large Scale Systems written by Vassil Alexandrov and published by . This book was released on 2015-11-15 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: SC15: The International Conference for High Performance Computing, Networking, Storage and Analysis Nov 15, 2015-Nov 20, 2015 Austin, USA. You can view more information about this proceeding and all of ACM�s other published conference proceedings from the ACM Digital Library: http://www.acm.org/dl.