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© 2019

Broad Learning Through Fusions

An Application on Social Networks

  • This book provides an introduction to broad learning, focusing on the fundamental concepts, learning tasks, and methodologies to build learning models for data fusion, and knowledge discovery.

  • It examines how the introduced broad learning approaches can be applied for effective data fusion and knowledge discovery on online social networks.

  • The book Introduces the social network alignment task and learning algorithms based on three different learning settings.

  • It provides a comprehensive introduction to the several well-known knowledge discovery problems with the fused information from multiple online social networks

Textbook

Table of contents

  1. Front Matter
    Pages i-xv
  2. Background Introduction

    1. Front Matter
      Pages 1-1
    2. Jiawei Zhang, Philip S. Yu
      Pages 3-17
    3. Jiawei Zhang, Philip S. Yu
      Pages 19-75
    4. Jiawei Zhang, Philip S. Yu
      Pages 77-126
  3. Information Fusion: Social Network Alignment

    1. Front Matter
      Pages 127-127
    2. Jiawei Zhang, Philip S. Yu
      Pages 129-164
    3. Jiawei Zhang, Philip S. Yu
      Pages 165-202
    4. Jiawei Zhang, Philip S. Yu
      Pages 203-226
  4. Broad Learning: Knowledge Discovery Across Aligned Networks

    1. Front Matter
      Pages 227-227
    2. Jiawei Zhang, Philip S. Yu
      Pages 229-273
    3. Jiawei Zhang, Philip S. Yu
      Pages 275-314
    4. Jiawei Zhang, Philip S. Yu
      Pages 315-349
    5. Jiawei Zhang, Philip S. Yu
      Pages 351-384
    6. Jiawei Zhang, Philip S. Yu
      Pages 385-413
  5. Future Directions

    1. Front Matter
      Pages 415-415
    2. Jiawei Zhang, Philip S. Yu
      Pages 417-419

About this book

Introduction

This book offers a clear and comprehensive introduction to broad learning, one of the novel learning problems studied in data mining and machine learning. Broad learning aims at fusing multiple large-scale information sources of diverse varieties together, and carrying out synergistic data mining tasks across these fused sources in one unified analytic. This book takes online social networks as an application example to introduce the latest alignment and knowledge discovery algorithms. Besides the overview of broad learning, machine learning and social network basics, specific topics covered in this book include network alignment, link prediction, community detection, information diffusion, viral marketing, and network embedding.

Keywords

data mining machine learning fusion learning social networks network alignment

Authors and affiliations

  1. 1.Department of Computer ScienceFlorida State UniversityTallahasseeUSA
  2. 2.Department of Computer ScienceUniversity of IllinoisChicagoUSA

About the authors

Jiawei Zhang is Assistant Professor in the Department of Computer Science at Florida State University. In 2017 he founded IFM Lab, a research oriented academic laboratory, providing the latest information on fusion learning and data mining research works and application tools to both academia and industry.

Philip S. Yu is Professor in the Department of Computer Science at the University of Illinois at Chicago and also holds the Wexler Chair in Information and Technology. He was manager of the Software Tools and Techniques group at the IBM Thomas J. Watson Research Center. Dr. Yu has published more than 500 papers in refereed journals and conferences. He holds or has applied for more than 300 US patents. 


Bibliographic information

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