Friday, October 28, 2016

Network Analysis and Social Network Analysis - Bibliography and Videos




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http://snap.stanford.edu/

http://snap.stanford.edu/class/cs224w-2015/projects.html

https://web.stanford.edu/class/cs224w/

https://web.stanford.edu/class/cs224w/intro_handout/intro_handout.pdf

http://historicalnetworkresearch.org/resources/first-steps/

Network Structure Inference, A Survey: Motivations, Methods, and
Applications
Ivan Brugere, University of Illinois at Chicago
Brian Gallagher, Lawrence Livermore National Laboratory
Tanya Y. Berger-Wolf, University of Illinois at Chicago
https://arxiv.org/pdf/1610.00782.pdf


Social Network Analysis: Methods and Applications

Stanley Wasserman, Katherine Faust
Cambridge University Press, 25-Nov-1994 - Social Science - 825 pages


Social network analysis, which focuses on relationships among social entities, is used widely in the social and behavioral sciences, as well as in economics, marketing, and industrial engineering. Social Network Analysis: Methods and Applications reviews and discusses methods for the analysis of social networks with a focus on applications of these methods to many substantive examples. As the first book to provide a comprehensive coverage of the methodology and applications of the field, this study is both a reference book and a textbook.
https://books.google.co.in/books?hl=en&lr=&id=CAm2DpIqRUIC

The structure and function of complex networks
M. E. J. Newman
Department of Physics, University of Michigan, Ann Arbor, MI 48109, U.S.A. and
Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, U.S.A.



Analyzing Participation of Students in Online Courses Using Social
Network Analysis Techniques
Reihaneh Rabbany k., Mansoureh Takaffoli and Osmar R. Za¨─▒ane,
Department of Computing Science, University of Alberta, Canada
rabbanyk,takaffol,zaiane@ualberta.ca

Social Network Analysis and Mining to Support the
Assessment of On-line Student Participation


Big Data over Networks

Shuguang Cui, Alfred O. Hero, III, Zhi-Quan Luo
Cambridge University Press, 14-Jan-2016 - Computers - 457 pages


Utilising both key mathematical tools and state-of-the-art research results, this text explores the principles underpinning large-scale information processing over networks and examines the crucial interaction between big data and its associated communication, social and biological networks. Written by experts in the diverse fields of machine learning, optimisation, statistics, signal processing, networking, communications, sociology and biology, this book employs two complementary approaches: first analysing how the underlying network constrains the upper-layer of collaborative big data processing, and second, examining how big data processing may boost performance in various networks. Unifying the broad scope of the book is the rigorous mathematical treatment of the subjects, which is enriched by in-depth discussion of future directions and numerous open-ended problems that conclude each chapter. Readers will be able to master the fundamental principles for dealing with big data over large systems, making it essential reading for graduate students, scientific researchers and industry practitioners alike.


Networks, Crowds, and Markets: Reasoning about a Highly Connected World
Full Book
David Easley
Dept. of Economics
Cornell University

Jon Kleinberg
Dept. of Computer Science
Cornell University
Cambridge University Press, 2010
Draft version: June 10, 2010.

Fundamentals of Predictive Text Mining

Sholom M. Weiss, Nitin Indurkhya, Tong Zhang
Springer, 07-Sep-2015 - Computers - 239 pages


This successful textbook on predictive text mining offers a unified perspective on a rapidly evolving field, integrating topics spanning the varied disciplines of data science, machine learning, databases, and computational linguistics. Serving also as a practical guide, this unique book provides helpful advice illustrated by examples and case studies.

This highly anticipated second edition has been thoroughly revised and expanded with new material on deep learning, graph models, mining social media, errors and pitfalls in big data evaluation, Twitter sentiment analysis, and dependency parsing discussion. The fully updated content also features in-depth discussions on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation.

Topics and features: presents a comprehensive, practical and easy-to-read introduction to text mining; includes chapter summaries, useful historical and bibliographic remarks, and classroom-tested exercises for each chapter; explores the application and utility of each method, as well as the optimum techniques for specific scenarios; provides several descriptive case studies that take readers from problem description to systems deployment in the real world; describes methods that rely on basic statistical techniques, thus allowing for relevance to all languages (not just English); contains links to free downloadable industrial-quality text-mining software and other supplementary instruction material.

Fundamentals of Predictive Text Mining is an essential resource for IT professionals and managers, as well as a key text for advanced undergraduate computer science students and beginning graduate students.

http://www.leonidzhukov.net/hse/2016/sna/


Advanced Database Marketing: Innovative Methodologies and Applications for Managing Customer Relationships

Koen W. De Bock
Routledge, Mar 23, 2016 - 348 pages
https://books.google.co.in/books?id=4hHPCwAAQBAJ



1 comment:

  1. http://www.kidsfront.com/academics/class.html some helpful articles are available here…please look into it

    ReplyDelete