Improving Gaussian Embedding for Extracting Local Semantic Connectivity in Networks
Gaussian embedding in unsupervised graph representation learning aims to embed vertices into Gaussian distributions. Downstream tasks such as link prediction and node classification can be efficiently computed on Gaussian distributions. Existing Gaussian
- PDF / 32,275,541 Bytes
- 296 Pages / 439.37 x 666.142 pts Page_size
- 55 Downloads / 200 Views
Yunmook Nah · Chulyun Kim · Seon Ho Kim · Yang-Sae Moon · Steven Euijong Whang (Eds.)
Database Systems for Advanced Applications DASFAA 2020 International Workshops BDMS, SeCoP, BDQM, GDMA, and AIDE Jeju, South Korea, September 24–27, 2020 Proceedings
Lecture Notes in Computer Science Founding Editors Gerhard Goos Karlsruhe Institute of Technology, Karlsruhe, Germany Juris Hartmanis Cornell University, Ithaca, NY, USA
Editorial Board Members Elisa Bertino Purdue University, West Lafayette, IN, USA Wen Gao Peking University, Beijing, China Bernhard Steffen TU Dortmund University, Dortmund, Germany Gerhard Woeginger RWTH Aachen, Aachen, Germany Moti Yung Columbia University, New York, NY, USA
12115
More information about this series at http://www.springer.com/series/7409
Yunmook Nah Chulyun Kim Seon Ho Kim Yang-Sae Moon Steven Euijong Whang (Eds.) •
•
•
•
Database Systems for Advanced Applications DASFAA 2020 International Workshops BDMS, SeCoP, BDQM, GDMA, and AIDE Jeju, South Korea, September 24–27, 2020 Proceedings
123
Editors Yunmook Nah Dankook University Yongin, Korea (Republic of) Seon Ho Kim Integrated Media Systems Center University of Southern California Los Angeles, USA
Chulyun Kim Department of IT Engineering Sookmyung Women's University Seoul, Korea (Republic of) Yang-Sae Moon Kangwon National University Chunchon, Korea (Republic of)
Steven Euijong Whang Korea Advanced Institute of Science and Technology Daejeon, Korea (Republic of)
ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Computer Science ISBN 978-3-030-59412-1 ISBN 978-3-030-59413-8 (eBook) https://doi.org/10.1007/978-3-030-59413-8 LNCS Sublibrary: SL3 – Information Systems and Applications, incl. Internet/Web, and HCI © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered compan
