Statistical And Machine Learning Approaches For Network Analysis

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Statistical and Machine Learning Approaches for Network Analysis

Matthias Dehmer,Subhash C. Basak
Statistical and Machine Learning Approaches for Network Analysis Book PDF

Author : Matthias Dehmer,Subhash C. Basak
Publisher : John Wiley & Sons
Page : 269 pages
File Size : 53,9 Mb
Release : 2012-06-26
Category : Mathematics
ISBN : 9781118346983

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Statistical and Machine Learning Approaches for Network Analysis by Matthias Dehmer,Subhash C. Basak Pdf

Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internationally renowned researchers in the field of interdisciplinary network theory, the book presents current and classical methods to analyze networks statistically. Methods from machine learning, data mining, and information theory are strongly emphasized throughout. Real data sets are used to showcase the discussed methods and topics, which include: A survey of computational approaches to reconstruct and partition biological networks An introduction to complex networks—measures, statistical properties, and models Modeling for evolving biological networks The structure of an evolving random bipartite graph Density-based enumeration in structured data Hyponym extraction employing a weighted graph kernel Statistical and Machine Learning Approaches for Network Analysis is an excellent supplemental text for graduate-level, cross-disciplinary courses in applied discrete mathematics, bioinformatics, pattern recognition, and computer science. The book is also a valuable reference for researchers and practitioners in the fields of applied discrete mathematics, machine learning, data mining, and biostatistics.

Neural Networks and Statistical Learning

Ke-Lin Du,M. N. S. Swamy
Neural Networks and Statistical Learning Book PDF

Author : Ke-Lin Du,M. N. S. Swamy
Publisher : Springer Nature
Page : 988 pages
File Size : 46,8 Mb
Release : 2019-09-12
Category : Mathematics
ISBN : 9781447174523

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Neural Networks and Statistical Learning by Ke-Lin Du,M. N. S. Swamy Pdf

This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and allows readers to gain a practical working understanding of the content. This updated new edition presents recently published results and includes six new chapters that correspond to the recent advances in computational learning theory, sparse coding, deep learning, big data and cloud computing. Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include: • multilayer perceptron; • the Hopfield network; • associative memory models;• clustering models and algorithms; • t he radial basis function network; • recurrent neural networks; • nonnegative matrix factorization; • independent component analysis; •probabilistic and Bayesian networks; and • fuzzy sets and logic. Focusing on the prominent accomplishments and their practical aspects, this book provides academic and technical staff, as well as graduate students and researchers with a solid foundation and comprehensive reference on the fields of neural networks, pattern recognition, signal processing, and machine learning.

Statistical Network Analysis: Models, Issues, and New Directions

Edoardo M. Airoldi,David M. Blei,Stephen E. Fienberg,Anna Goldenberg,Eric P. Xing,Alice X. Zheng
Statistical Network Analysis Models Issues and New Directions Book PDF

Author : Edoardo M. Airoldi,David M. Blei,Stephen E. Fienberg,Anna Goldenberg,Eric P. Xing,Alice X. Zheng
Publisher : Springer
Page : 200 pages
File Size : 52,9 Mb
Release : 2008-04-12
Category : Computers
ISBN : 9783540731337

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Statistical Network Analysis: Models, Issues, and New Directions by Edoardo M. Airoldi,David M. Blei,Stephen E. Fienberg,Anna Goldenberg,Eric P. Xing,Alice X. Zheng Pdf

This book constitutes the thoroughly refereed post-proceedings of the International Workshop on Statistical Network Analysis: Models, Issues, and New Directions held in Pittsburgh, PA, USA in June 2006 as associated event of the 23rd International Conference on Machine Learning, ICML 2006. It covers probabilistic methods for network analysis, paying special attention to model design and computational issues of learning and inference.

State of the Art Applications of Social Network Analysis

Fazli Can,Tansel Özyer,Faruk Polat
State of the Art Applications of Social Network Analysis Book PDF

Author : Fazli Can,Tansel Özyer,Faruk Polat
Publisher : Springer
Page : 372 pages
File Size : 45,6 Mb
Release : 2014-05-14
Category : Computers
ISBN : 9783319059129

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State of the Art Applications of Social Network Analysis by Fazli Can,Tansel Özyer,Faruk Polat Pdf

Social network analysis increasingly bridges the discovery of patterns in diverse areas of study as more data becomes available and complex. Yet the construction of huge networks from large data often requires entirely different approaches for analysis including; graph theory, statistics, machine learning and data mining. This work covers frontier studies on social network analysis and mining from different perspectives such as social network sites, financial data, e-mails, forums, academic research funds, XML technology, blog content, community detection and clique finding, prediction of user’s- behavior, privacy in social network analysis, mobility from spatio-temporal point of view, agent technology and political parties in parliament. These topics will be of interest to researchers and practitioners from different disciplines including, but not limited to, social sciences and engineering.

Statistical Learning Using Neural Networks

Basilio de Braganca Pereira,Calyampudi Radhakrishna Rao,Fabio Borges de Oliveira
Statistical Learning Using Neural Networks Book PDF

Author : Basilio de Braganca Pereira,Calyampudi Radhakrishna Rao,Fabio Borges de Oliveira
Publisher : CRC Press
Page : 234 pages
File Size : 53,9 Mb
Release : 2020-09-01
Category : Business & Economics
ISBN : 9780429775550

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Statistical Learning Using Neural Networks by Basilio de Braganca Pereira,Calyampudi Radhakrishna Rao,Fabio Borges de Oliveira Pdf

Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks starting from the basics and increasingly demanding more effort from readers, who can learn the theory and its applications in statistical methods with concrete Python code examples. It presents a wide range of widely used statistical methodologies, applied in several research areas with Python code examples, which are available online. It is suitable for scientists and developers as well as graduate students. Key Features: Discusses applications in several research areas Covers a wide range of widely used statistical methodologies Includes Python code examples Gives numerous neural network models This book covers fundamental concepts on Neural Networks including Multivariate Statistics Neural Networks, Regression Neural Network Models, Survival Analysis Networks, Time Series Forecasting Networks, Control Chart Networks, and Statistical Inference Results. This book is suitable for both teaching and research. It introduces neural networks and is a guide for outsiders of academia working in data mining and artificial intelligence (AI). This book brings together data analysis from statistics to computer science using neural networks.

Introduction to Statistical and Machine Learning Methods for Data Science

Carlos Andre Reis Pinheiro,Mike Patetta
Introduction to Statistical and Machine Learning Methods for Data Science Book PDF

Author : Carlos Andre Reis Pinheiro,Mike Patetta
Publisher : SAS Institute
Page : 169 pages
File Size : 51,6 Mb
Release : 2021-08-06
Category : Computers
ISBN : 9781953329622

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Introduction to Statistical and Machine Learning Methods for Data Science by Carlos Andre Reis Pinheiro,Mike Patetta Pdf

Boost your understanding of data science techniques to solve real-world problems Data science is an exciting, interdisciplinary field that extracts insights from data to solve business problems. This book introduces common data science techniques and methods and shows you how to apply them in real-world case studies. From data preparation and exploration to model assessment and deployment, this book describes every stage of the analytics life cycle, including a comprehensive overview of unsupervised and supervised machine learning techniques. The book guides you through the necessary steps to pick the best techniques and models and then implement those models to successfully address the original business need. No software is shown in the book, and mathematical details are kept to a minimum. This allows you to develop an understanding of the fundamentals of data science, no matter what background or experience level you have.

Probabilistic Foundations of Statistical Network Analysis

Harry Crane
Probabilistic Foundations of Statistical Network Analysis Book PDF

Author : Harry Crane
Publisher : CRC Press
Page : 432 pages
File Size : 40,5 Mb
Release : 2018-04-17
Category : Business & Economics
ISBN : 9781351807326

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Probabilistic Foundations of Statistical Network Analysis by Harry Crane Pdf

Probabilistic Foundations of Statistical Network Analysis presents a fresh and insightful perspective on the fundamental tenets and major challenges of modern network analysis. Its lucid exposition provides necessary background for understanding the essential ideas behind exchangeable and dynamic network models, network sampling, and network statistics such as sparsity and power law, all of which play a central role in contemporary data science and machine learning applications. The book rewards readers with a clear and intuitive understanding of the subtle interplay between basic principles of statistical inference, empirical properties of network data, and technical concepts from probability theory. Its mathematically rigorous, yet non-technical, exposition makes the book accessible to professional data scientists, statisticians, and computer scientists as well as practitioners and researchers in substantive fields. Newcomers and non-quantitative researchers will find its conceptual approach invaluable for developing intuition about technical ideas from statistics and probability, while experts and graduate students will find the book a handy reference for a wide range of new topics, including edge exchangeability, relative exchangeability, graphon and graphex models, and graph-valued Levy process and rewiring models for dynamic networks. The author’s incisive commentary supplements these core concepts, challenging the reader to push beyond the current limitations of this emerging discipline. With an approachable exposition and more than 50 open research problems and exercises with solutions, this book is ideal for advanced undergraduate and graduate students interested in modern network analysis, data science, machine learning, and statistics. Harry Crane is Associate Professor and Co-Director of the Graduate Program in Statistics and Biostatistics and an Associate Member of the Graduate Faculty in Philosophy at Rutgers University. Professor Crane’s research interests cover a range of mathematical and applied topics in network science, probability theory, statistical inference, and mathematical logic. In addition to his technical work on edge and relational exchangeability, relative exchangeability, and graph-valued Markov processes, Prof. Crane’s methods have been applied to domain-specific cybersecurity and counterterrorism problems at the Foreign Policy Research Institute and RAND’s Project AIR FORCE.

Big Data Analytics

Mrutyunjaya Panda,Ajith Abraham,Aboul Ella Hassanien
Big Data Analytics Book PDF

Author : Mrutyunjaya Panda,Ajith Abraham,Aboul Ella Hassanien
Publisher : CRC Press
Page : 255 pages
File Size : 42,5 Mb
Release : 2018-12-12
Category : Business & Economics
ISBN : 9781351622585

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Big Data Analytics by Mrutyunjaya Panda,Ajith Abraham,Aboul Ella Hassanien Pdf

Social networking has increased drastically in recent years, resulting in an increased amount of data being created daily. Furthermore, diversity of issues and complexity of the social networks pose a challenge in social network mining. Traditional algorithm software cannot deal with such complex and vast amounts of data, necessitating the development of novel analytic approaches and tools. This reference work deals with social network aspects of big data analytics. It covers theory, practices and challenges in social networking. The book spans numerous disciplines like neural networking, deep learning, artificial intelligence, visualization, e-learning in higher education, e-healthcare, security and intrusion detection.

Solving Large Scale Learning Tasks. Challenges and Algorithms

Stefan Michaelis,Nico Piatkowski,Marco Stolpe
Solving Large Scale Learning Tasks Challenges and Algorithms Book PDF

Author : Stefan Michaelis,Nico Piatkowski,Marco Stolpe
Publisher : Springer
Page : 387 pages
File Size : 45,9 Mb
Release : 2016-07-02
Category : Computers
ISBN : 9783319417066

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Solving Large Scale Learning Tasks. Challenges and Algorithms by Stefan Michaelis,Nico Piatkowski,Marco Stolpe Pdf

In celebration of Prof. Morik's 60th birthday, this Festschrift covers research areas that Prof. Morik worked in and presents various researchers with whom she collaborated. The 23 refereed articles in this Festschrift volume provide challenges and solutions from theoreticians and practitioners on data preprocessing, modeling, learning, and evaluation. Topics include data-mining and machine-learning algorithms, feature selection and feature generation, optimization as well as efficiency of energy and communication.

Statistical Inference and Machine Learning for Big Data

Mayer Alvo
Statistical Inference and Machine Learning for Big Data Book PDF

Author : Mayer Alvo
Publisher : Springer Nature
Page : 442 pages
File Size : 48,6 Mb
Release : 2022-11-30
Category : Mathematics
ISBN : 9783031067846

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Statistical Inference and Machine Learning for Big Data by Mayer Alvo Pdf

This book presents a variety of advanced statistical methods at a level suitable for advanced undergraduate and graduate students as well as for others interested in familiarizing themselves with these important subjects. It proceeds to illustrate these methods in the context of real-life applications in a variety of areas such as genetics, medicine, and environmental problems. The book begins in Part I by outlining various data types and by indicating how these are normally represented graphically and subsequently analyzed. In Part II, the basic tools in probability and statistics are introduced with special reference to symbolic data analysis. The most useful and relevant results pertinent to this book are retained. In Part III, the focus is on the tools of machine learning whereas in Part IV the computational aspects of BIG DATA are presented. This book would serve as a handy desk reference for statistical methods at the undergraduate and graduate level as well as be useful in courses which aim to provide an overview of modern statistics and its applications.

Cybersecurity Data Science

Scott Mongeau,Andrzej Hajdasinski
Cybersecurity Data Science Book PDF

Author : Scott Mongeau,Andrzej Hajdasinski
Publisher : Springer Nature
Page : 410 pages
File Size : 53,9 Mb
Release : 2021-10-01
Category : Computers
ISBN : 9783030748968

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Cybersecurity Data Science by Scott Mongeau,Andrzej Hajdasinski Pdf

This book encompasses a systematic exploration of Cybersecurity Data Science (CSDS) as an emerging profession, focusing on current versus idealized practice. This book also analyzes challenges facing the emerging CSDS profession, diagnoses key gaps, and prescribes treatments to facilitate advancement. Grounded in the management of information systems (MIS) discipline, insights derive from literature analysis and interviews with 50 global CSDS practitioners. CSDS as a diagnostic process grounded in the scientific method is emphasized throughout Cybersecurity Data Science (CSDS) is a rapidly evolving discipline which applies data science methods to cybersecurity challenges. CSDS reflects the rising interest in applying data-focused statistical, analytical, and machine learning-driven methods to address growing security gaps. This book offers a systematic assessment of the developing domain. Advocacy is provided to strengthen professional rigor and best practices in the emerging CSDS profession. This book will be of interest to a range of professionals associated with cybersecurity and data science, spanning practitioner, commercial, public sector, and academic domains. Best practices framed will be of interest to CSDS practitioners, security professionals, risk management stewards, and institutional stakeholders. Organizational and industry perspectives will be of interest to cybersecurity analysts, managers, planners, strategists, and regulators. Research professionals and academics are presented with a systematic analysis of the CSDS field, including an overview of the state of the art, a structured evaluation of key challenges, recommended best practices, and an extensive bibliography.

Computational Statistics

Geof H. Givens,Jennifer A. Hoeting
Computational Statistics Book PDF

Author : Geof H. Givens,Jennifer A. Hoeting
Publisher : John Wiley & Sons
Page : 496 pages
File Size : 44,6 Mb
Release : 2012-11-06
Category : Mathematics
ISBN : 9780470533314

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Computational Statistics by Geof H. Givens,Jennifer A. Hoeting Pdf

This new edition continues to serve as a comprehensive guide to modern and classical methods of statistical computing. The book is comprised of four main parts spanning the field: Optimization Integration and Simulation Bootstrapping Density Estimation and Smoothing Within these sections,each chapter includes a comprehensive introduction and step-by-step implementation summaries to accompany the explanations of key methods. The new edition includes updated coverage and existing topics as well as new topics such as adaptive MCMC and bootstrapping for correlated data. The book website now includes comprehensive R code for the entire book. There are extensive exercises, real examples, and helpful insights about how to use the methods in practice.

Graph Representation Learning

William L. William L. Hamilton
Graph Representation Learning Book PDF

Author : William L. William L. Hamilton
Publisher : Springer Nature
Page : 141 pages
File Size : 41,5 Mb
Release : 2022-06-01
Category : Computers
ISBN : 9783031015885

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Graph Representation Learning by William L. William L. Hamilton Pdf

Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

Computational Network Theory

Matthias Dehmer,Frank Emmert-Streib,Stefan Pickl
Computational Network Theory Book PDF

Author : Matthias Dehmer,Frank Emmert-Streib,Stefan Pickl
Publisher : John Wiley & Sons
Page : 200 pages
File Size : 53,9 Mb
Release : 2015-04-28
Category : Medical
ISBN : 9783527691531

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Computational Network Theory by Matthias Dehmer,Frank Emmert-Streib,Stefan Pickl Pdf

This comprehensive introduction to computational network theory as a branch of network theory builds on the understanding that such networks are a tool to derive or verify hypotheses by applying computational techniques to large scale network data. The highly experienced team of editors and high-profile authors from around the world present and explain a number of methods that are representative of computational network theory, derived from graph theory, as well as computational and statistical techniques. With its coherent structure and homogenous style, this reference is equally suitable for courses on computational networks.

Topics at the Frontier of Statistics and Network Analysis

Eric D. Kolaczyk
Topics at the Frontier of Statistics and Network Analysis Book PDF

Author : Eric D. Kolaczyk
Publisher : Cambridge University Press
Page : 214 pages
File Size : 42,8 Mb
Release : 2017-08-10
Category : Mathematics
ISBN : 9781108305617

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Topics at the Frontier of Statistics and Network Analysis by Eric D. Kolaczyk Pdf

This snapshot of the current frontier of statistics and network analysis focuses on the foundational topics of modeling, sampling, and design. Primarily for graduate students and researchers in statistics and closely related fields, emphasis is not only on what has been done, but on what remains to be done.