Skip to content

Due to system maintenance, purchasing is not available at this time. We are working to fix the issue and apologise for any inconvenience caused.

Your Cart

×

You have 0 items in your cart.

Register Sign in Wishlist

Algorithms and Models for Network Data and Link Analysis

  • Date Published: July 2016
  • availability: In stock
  • format: Hardback
  • isbn: 9781107125773

Hardback

Add to wishlist

Other available formats:
eBook


Looking for an examination copy?

If you are interested in the title for your course we can consider offering an examination copy. To register your interest please contact collegesales@cambridge.org providing details of the course you are teaching.

Description
Product filter button
Description
Contents
Resources
Courses
About the Authors
  • Network data are produced automatically by everyday interactions - social networks, power grids, and links between data sets are a few examples. Such data capture social and economic behavior in a form that can be analyzed using powerful computational tools. This book is a guide to both basic and advanced techniques and algorithms for extracting useful information from network data. The content is organized around 'tasks', grouping the algorithms needed to gather specific types of information and thus answer specific types of questions. Examples include similarity between nodes in a network, prestige or centrality of individual nodes, and dense regions or communities in a network. Algorithms are derived in detail and summarized in pseudo-code. The book is intended primarily for computer scientists, engineers, statisticians and physicists, but it is also accessible to network scientists based in the social sciences. Matlab/Octave code illustrating some of the algorithms will be available at: http://www.cambridge.org/9781107125773.

    • Unifies algorithms from diverse fields, including applied mathematics, computer science and physics
    • The task-based approach focuses on what information needs to be extracted, then on how to do it
    • Derives algorithms in detail and summarizes in pseudo-code to support implementation and adaptation
    Read more

    Reviews & endorsements

    "This is a remarkable book that contains a coherent and unified presentation of many recent network data analysis concepts and algorithms. Rich with details and references, this is a book from which faculty and students alike will learn a lot!"
    Vincent Blondel, Université catholique de Louvain

    "An impressive compilation of motivation, derivations, and algorithms for a wealth of methods relevant to assessing distance and (dis)similarity, importance, labeling, and clustering of network nodes and links - tasks fundamental to network analysis in practice. The gathering of diverse elements from random walks, kernels, and other interrelated topics is particularly welcome."
    Eric D. Kolaczyk, Boston University

    "This is a reader-friendly up-to-date book covering all the major topics in static network data analysis. It both exposes the reader to the most advanced ideas in the field and provides the researcher with a toolbox of techniques to explore various structures: models involving the graph Laplacian, regularization methods, and Markov interpretations feature in this toolbox, among others."
    Pavel Chebotarev, Institute of Control Sciences, Russian Academy of Sciences

    See more reviews

    Customer reviews

    Not yet reviewed

    Be the first to review

    Review was not posted due to profanity

    ×

    , create a review

    (If you're not , sign out)

    Please enter the right captcha value
    Please enter a star rating.
    Your review must be a minimum of 12 words.

    How do you rate this item?

    ×

    Product details

    • Date Published: July 2016
    • format: Hardback
    • isbn: 9781107125773
    • length: 543 pages
    • dimensions: 261 x 184 x 33 mm
    • weight: 1.15kg
    • contains: 14 b/w illus. 7 tables
    • availability: In stock
  • Table of Contents

    1. Preliminaries and notation
    2. Similarity/proximity measures between nodes
    3. Families of dissimilarity between nodes
    4. Centrality measures on nodes and edges
    5. Identifying prestigious nodes
    6. Labeling nodes: within-network classification
    7. Clustering nodes
    8. Finding dense regions
    9. Bipartite graph analysis
    10. Graph embedding.

  • Authors

    François Fouss, Université Catholique de Louvain, Belgium
    François Fouss received his PhD from the Université catholique de Louvain, Belgium, where he is now Professor of Computer Science. His research and teaching interests include artificial intelligence, data mining, machine learning, pattern recognition, and natural language processing, with a focus on graph-based techniques.

    Marco Saerens, Université Catholique de Louvain, Belgium
    Marco Saerens received his PhD from the Université Libre de Bruxelles, Belgium. He is now Professor of Computer Science at the Université catholique de Louvain, Belgium. His research and teaching interests include artificial intelligence, data mining, machine learning, pattern recognition, and natural language processing, with a focus on graph-based techniques.

    Masashi Shimbo, Nara Institute of Science and Technology, Japan
    Masashi Shimbo received his PhD from Kyoto University, Japan. He is now Associate Professor at the Graduate School of Information Science, Nara Institute of Science and Technology, Japan. His research and teaching interests include artificial intelligence, data mining, machine learning, pattern recognition, and natural language processing, with a focus on graph-based techniques.

Sign In

Please sign in to access your account

Cancel

Not already registered? Create an account now. ×

Sorry, this resource is locked

Please register or sign in to request access. If you are having problems accessing these resources please email lecturers@cambridge.org

Register Sign in
Please note that this file is password protected. You will be asked to input your password on the next screen.

» Proceed

You are now leaving the Cambridge University Press website. Your eBook purchase and download will be completed by our partner www.ebooks.com. Please see the permission section of the www.ebooks.com catalogue page for details of the print & copy limits on our eBooks.

Continue ×

Continue ×

Continue ×

Find content that relates to you

Are you sure you want to delete your account?

This cannot be undone.

Cancel

Thank you for your feedback which will help us improve our service.

If you requested a response, we will make sure to get back to you shortly.

×
Please fill in the required fields in your feedback submission.
×