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 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 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 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 Working with Network Data written by James Bagrow and published by Cambridge University Press. This book was released on 2024-05-31 with total page 555 pages. Available in PDF, EPUB and Kindle. Book excerpt: Drawing examples from real-world networks, this essential book traces the methods behind network analysis and explains how network data is first gathered, then processed and interpreted. The text will equip you with a toolbox of diverse methods and data modelling approaches, allowing you to quickly start making your own calculations on a huge variety of networked systems. This book sets you up to succeed, addressing the questions of what you need to know and what to do with it, when beginning to work with network data. The hands-on approach adopted throughout means that beginners quickly become capable practitioners, guided by a wealth of interesting examples that demonstrate key concepts. Exercises using real-world data extend and deepen your understanding, and develop effective working patterns in network calculations and analysis. Suitable for both graduate students and researchers across a range of disciplines, this novel text provides a fast-track to network data expertise.

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 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 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 Frontiers in Massive Data Analysis written by National Research Council and published by National Academies Press. This book was released on 2013-09-03 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.

Download or read book A Hands on Introduction to Big Data Analytics written by Funmi Obembe and published by SAGE Publications Limited. This book was released on 2024-02-23 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: This practical textbook offers a hands-on introduction to big data analytics, helping you to develop the skills required to hit the ground running as a data professional. It complements theoretical foundations with an emphasis on the application of big data analytics, illustrated by real-life examples and datasets. Containing comprehensive coverage of all the key topics in this area, this book uses open-source technologies and examples in Python and Apache Spark. Learning features include: - Ethics by Design encourages you to consider data ethics at every stage. - Industry Insights facilitate a deeper understanding of the link between what you are studying and how it is applied in industry. - Datasets, questions, and exercises give you the opportunity to apply your learning. Dr Funmi Obembe is the Head of Technology at the Faculty of Arts, Science and Technology, University of Northampton. Dr Ofer Engel is a Data Scientist at the University of Groningen.

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 Scalable Fuzzy Algorithms for Data Management and Analysis Methods and Design written by Laurent, Anne and published by IGI Global. This book was released on 2009-10-31 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book presents up-to-date techniques for addressing data management problems with logic and memory use"--Provided by publisher.

Download or read book Statistical Performance Analysis and Modeling Techniques for Nanometer VLSI Designs written by Ruijing Shen and published by Springer Science & Business Media. This book was released on 2012-03-18 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers statistical modeling and analysis of VLSI systems with a focus on interconnects, on-chip power grids and clock networks and analog/mixed-signal circuits. It offers an analysis of each algorithm with applications in real circuit design.

Download or read book Euro Par 2015 Parallel Processing written by Jesper Larsson Träff and published by Springer. This book was released on 2015-07-24 with total page 717 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 21st International Conference on Parallel and Distributed Computing, Euro-Par 2015, held in Vienna, Austria, in August 2015. The 51 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 190 submissions. The papers are organized in the following topical sections: support tools and environments; performance modeling, prediction and evaluation; scheduling and load balancing; architecture and compilers; parallel and distributed data management; grid, cluster and cloud computing; distributed systems and algorithms; parallel and distributed programming, interfaces and languages; multi- and many-core programming; theory and algorithms for parallel computation; numerical methods and applications; and accelerator computing.

Download or read book Big Data Analysis New Algorithms for a New Society written by Nathalie Japkowicz and published by Springer. This book was released on 2015-12-16 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Proceedings of the Fourteenth Annual ACM SIAM Symposium on Discrete Algorithms written by and published by SIAM. This book was released on 2003-01-01 with total page 896 pages. Available in PDF, EPUB and Kindle. Book excerpt: From the January 2003 symposium come just over 100 papers addressing a range of topics related to discrete algorithms. Examples of topics covered include packing Steiner trees, counting inversions in lists, directed scale-free graphs, quantum property testing, and improved results for directed multicut. The papers were not formally refereed, but attempts were made to verify major results. Annotation (c)2003 Book News, Inc., Portland, OR (booknews.com)