Social Network Data Analytics
Social network analysis applications have experienced tremendous advances within the last few years due in part to increasing trends towards users interacting with each other on the internet. Social networks are organized as graphs, and the data on social
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Charu C. Aggarwal Editor
Social Network Data Analytics
1C
Editor Charu C. Aggarwal IBM Thomas J. Watson Research Center 19 Skyline Drive Hawthorne, NY 10532, USA [email protected]
ISBN 978-1-4419-8461-6 e-ISBN 978-1-4419-8462-3 DOI 10.1007/978-1-4419-8462-3 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2011922836 © Springer Science+Business Media, LLC 2011 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Contents
Preface 1 An Introduction to Social Network Data Analytics Charu C. Aggarwal 1. Introduction 2. Online Social Networks: Research Issues 3. Research Topics in Social Networks 4. Conclusions and Future Directions References 2 Statistical Properties of Social Networks Mary McGlohon, Leman Akoglu and Christos Faloutsos 1. Preliminaries 1.1 De¿nitions 1.2 Data description 2. Static Properties 2.1 Static Unweighted Graphs 2.2 Static Weighted Graphs 3. Dynamic Properties 3.1 Dynamic Unweighted Graphs 3.2 Dynamic Weighted Graphs 4. Conclusion References 3 Random Walks in Social Networks and their Applications: A Survey Purnamrita Sarkar and Andrew W. Moore 1. Introduction 2. Random Walks on Graphs: Background 2.1 Random Walk based Proximity Measures 2.2 Other Graph-based Proximity Measures 2.3 Graph-theoretic Measures for Semi-supervised Learning 2.4 Clustering with random walk based measures 3. Related Work: Algorithms 3.1 Algorithms for Hitting and Commute Times 3.2 Algorithms for Computing Personalized Pagerank and Simrank
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SOCIAL NETWORK DATA ANALYTICS 3.3 Algorithms for Computing Harmonic Functions Related Work: Applications 4.1 Application in Computer Vision 4.2 Text Analysis 4.3 Collaborative Filtering 4.4 Combating Webspam 5. Related Work: Evaluation and datasets 5.1 Evaluation: Link Prediction 5.2 Publicly Available Data Sources 6. Conclusion and Future Work References 4.
4 Community Discovery in Social Networks: Applications, Methods and Emerging Trends S. Parthasarathy, Y. Ruan and V. Satuluri 1. Introduction 2. Communities in Context 3. Core Methods 3.1 Quality Functions 3.2 The Kernighan-Lin(KL) algorithm 3.3 Agglomerative/Divisive Algorithms 3.4 Spectral Algorithms 3.5 Multi-level Graph Partitioning 3.6
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