Statistical and Machine Learning Approaches for Network Analysis Statistical and Machine Learning Approaches for Network Analysis
Wiley Series in Computational Statistics

Statistical and Machine Learning Approaches for Network Analysis

    • $114.99
    • $114.99

Publisher Description

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.

GENRE
Science & Nature
RELEASED
2012
June 26
LANGUAGE
EN
English
LENGTH
344
Pages
PUBLISHER
Wiley
SELLER
John Wiley & Sons, Inc.
SIZE
5.8
MB
Proceedings of NetSci-X 2020: Sixth International Winter School and Conference on Network Science Proceedings of NetSci-X 2020: Sixth International Winter School and Conference on Network Science
2020
Link Mining: Models, Algorithms, and Applications Link Mining: Models, Algorithms, and Applications
2010
Handbook of Large-Scale Random Networks Handbook of Large-Scale Random Networks
2010
Modeling and Analysis of Bio-molecular Networks Modeling and Analysis of Bio-molecular Networks
2020
The Structure of Complex Networks The Structure of Complex Networks
2011
Models, Algorithms and Technologies for Network Analysis Models, Algorithms and Technologies for Network Analysis
2014
Frontiers in Data Science Frontiers in Data Science
2017
Elements of Data Science, Machine Learning, and Artificial Intelligence Using R Elements of Data Science, Machine Learning, and Artificial Intelligence Using R
2023
Modern and Interdisciplinary Problems in Network Science Modern and Interdisciplinary Problems in Network Science
2018
Entrepreneurial Complexity Entrepreneurial Complexity
2019
Big Data of Complex Networks Big Data of Complex Networks
2016
Graph Polynomials Graph Polynomials
2016
Computational Statistics Computational Statistics
2012
Bayesian Modeling Using WinBUGS Bayesian Modeling Using WinBUGS
2011
Clustering Methodology for Symbolic Data Clustering Methodology for Symbolic Data
2019
Understanding Computational Bayesian Statistics Understanding Computational Bayesian Statistics
2011
Advanced Markov Chain Monte Carlo Methods Advanced Markov Chain Monte Carlo Methods
2011
Multivariate Nonparametric Regression and Visualization Multivariate Nonparametric Regression and Visualization
2014