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Deep Learning on Graphs > 인공지능

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Deep Learning on Graphs
추천도서히트도서
판매가격 59,000원
저자 Yao Ma, Jiliang Tang
도서종류 외국도서
출판사 Cambridge University Press
발행언어 영어
발행일 2021
페이지수 400
ISBN 9781108831741
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    도서 상세설명

    About the Author

    Yao Ma is a PhD student of the Department of Computer Science and Engineering at Michigan State University (MSU). He is the recipient of the Outstanding Graduate Student Award and FAST Fellowship at MSU. He has published papers in top conferences such as WSDM, ICDM, SDM, WWW, IJCAI, SIGIR and KDD, which have been cited hundreds of times. He is the leading organizer and presenter of tutorials on GNNs at AAAI'20, KDD'20 and AAAI'21, which received huge attention and wide acclaim. He has served as Program Committee Members/Reviewers in many well-known conferences and magazines such as AAAI, BigData, IJCAI, TWEB, TKDD and TPAMI.

     

    Jiliang Tang is Assistant Professor in the Department of Computer Science and Engineering at Michigan State University. Previously, he was a research scientist in Yahoo Research. He received the 2020 SIGKDD Rising Star Award, 2020 Distinguished Withrow Research Award, 2019 NSF Career Award, the 2019 IJCAI Early Career Invited Talk and 7 best paper (runnerup) awards. He has organized top data science conferences including KDD, WSDM and SDM, and is associate editor of the TKDD journal. His research has been published in highly ranked journals and top conferences, and received more than 12,000 citations with h-index 55 and extensive media coverage.

     

    Table of Contents

    1. Deep Learning on Graphs: An Introduction;

    2. Foundation of Graphs;

    3. Foundation of Deep Learning;

    4. Graph Embedding;

    5. Graph Neural Networks;

    6. Robust Graph Neural Networks;

    7. Scalable Graph Neural Networks; 8. Graph Neural Networks for Complex Graphs;

    9. Beyond GNNs: More Deep Models for Graphs;

    10. Graph Neural Networks in Natural Language Processing;

    11. Graph Neural Networks in Computer Vision;

    12. Graph Neural Networks in Data Mining; 13. Graph Neural Networks in Biochemistry and Healthcare;

    14. Advanced Topics in Graph Neural Networks;

    15. Advanced Applications in Graph Neural Networks.

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