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Golbeck - Analyzing the Social Web

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Analyzing the Social Web provides a framework for the analysis of public data currently available and being generated by social networks and social media, like Facebook, Twitter, and Foursquare. Access and analysis of this public data about people and their connections to one another allows for new applications of traditional social network analysis techniques that let us identify things like who are the most important or influential people in a network, how things will spread through the network, and the nature of peoples relationships. Analyzing the Social Web introduces you to these techniques, shows you their application to many different types of social media, and discusses how social media can be used as a tool for interacting with the online public. Presents interactive social applications on the web, and the types of analysis that are currently conducted in the study of social media. Covers the basics of network structures for beginners, including measuring methods for describing nodes, edges, and parts of the network. Discusses the major categories of social media applications or phenomena and shows how the techniques presented can be applied to analyze and understand the underlying data. Provides an introduction to information visualization, particularly network visualization techniques, and methods for using them to identify interesting features in a network, generate hypotheses for analysis, and recognize patterns of behavior. Includes a supporting website with lecture slides, exercises, and downloadable social network data sets that can be used can be used to apply the techniques presented in the book. Read more...
Abstract: Provides a framework for analysis of public data and being generated by social networks and social media, like Facebook, Twitter, and Foursquare. This book introduces you to these techniques, shows you their application to different types of social media, and discusses how social media can be used as a tool for interacting with online public. Read more...

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Analyzing the Social Web Jennifer Golbeck Table of Contents Copyright - photo 1
Analyzing the Social Web

Jennifer Golbeck

Table of Contents Copyright Acquiring Editor Steve Elliot Editorial Project - photo 2

Table of Contents
Copyright

Acquiring Editor: Steve Elliot

Editorial Project Manager: Lindsay Lawrence

Project Manager: Punithavathy Govindaradjane

Designer: Mark Rogers

Morgan Kaufmann is an imprint of Elsevier

225 Wyman Street, Waltham, MA 02451, USA

Copyright 2013 Elsevier Inc. All rights reserved

No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publishers permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions.

This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein).

Notices

Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods or professional practices, may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information or methods described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility.

To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein.

Library of Congress Cataloging-in-Publication Data

Golbeck, Jennifer.

Analyzing the social web/Jennifer Golbeck.First edition.

pages cm.

Includes bibliographical references and index.

ISBN 978-0-12-405531-5 (alk. paper)

1.Social media. 2.Social networks. 3.Human-computer interaction. I. Title.

HM742.G65 2013

302.3dc23

2012049046

British Library Cataloguing-in-Publication Data

A catalogue record for this book is available from the British Library

ISBN: 978-0-12-405531-5

Printed and bound in the United States of America

13 14 15 16 1710 9 8 7 6 5 4 3 2 1

For information on all MK publications visit our website at wwwmkpcom - photo 3

For information on all MK publications visit our website at www.mkp.com

Dedication

To Ingo

List of Figures
NumberFigure
A sample social network.
The Gephi interface.
The NodeXL interface.
The five co-stars of Apollo 13. Each is represented as a node in the network.
The edges connect actors who were in movies together.
A labeled graph where the edges indicate at least one movie that the actors have been in together, not including Apollo 13.
A weighted graph where weights are indicated both as numbers and by the thickness of the edge. In this graph, weight indicates how many movies the actors have been in together.
Two ways of drawing a directed network. The edge from A to B is directed only one way. The edge from A to C goes in both directions and can be drawn either as one edge with two arrow heads (left) or as two edges pointing in opposite directions (right).
A social network with a singleton, dyad, and triad.
A sample undirected network.
(a) The 1-degree egocentric network of D, (b) the 1.5-degree egocentric network of D, (c) the 1.5 egocentric network of D with D excluded, and (d) the 2-degree egocentric network of D.
A sample undirected network.
A sample directed graph.
The node at the center of the cluster in the upper right would have a high degree centrality, even though it is far from the dense center of the network.
A sample network.
The degree distribution for the graph shown in .
Network (a) on the left has fewer edges than network (b) on the right. Since they both have the same number of nodes and thus the same number of possible edges, network (b) is more dense.
The 1.5-diameter egocentric networks for nodes A (a) and B (b) from .
A sample network with a connectivity of 2.
A regular graph. Each node is connected to the neighbor directly next to it and two steps away in the layout.
A random graph, with the same number of nodes and edges as the regular graph shown in .
(a) shows the stages of a regular graph becoming more random by removing and randomly reconnecting some of the edges. (b) shows how the clustering (C) remains high while the average shortest path length (L) quickly drops to low values as the graph becomes more random. The variable p indicates the probability of random edge rewiring.
Without conscious analysis, it is easy to pick out the circle as an anomaly in the pattern.
A single outlier point at value 2 on the x-axis is easy to see separated from the pattern of values.
A sample network visualization.
A random layout of the graph shown in .
A circular graph layout for the same graph shown in .
A grid layout of the modes in the sample graph.
A layout of the sample network using the Force Atlas algorithm.
The graph laid out with the Harel-Koren Fast multiscale algorithm.
A layout that groups clusters into boxes, sized by the size of the cluster, and shows links between boxes.
A network of YouTube videos with the node labels shown.
Color-coding nodes according to their degree, with higher degree shown by darker nodes.
A graph indicating clustering coefficient with node size and degree with node color.
The sample network with edge width indicating the weight on each edge. Note that the central node has medium-strength relationships with most neighbors, but weak ones to the cluster in the upper right and the chain in the lower right. The chain of nodes in the lower right have high weights on the edges connecting them.
A network of YouTube videos where color indicates the community or cluster to which each node belongs.
A network with 11,000 nodes and 40,000 edges.
A network of senators (nodes) with edges connecting senators who have voted the same way at least 40% of the time. The network is very dense, so it is not possible to see any interesting patterns.
The same network of senators as shown in , now filtered to include only edges between senators who have voted the same way on at least two-thirds of bills.
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