Identifying Vital Nodes in Social Networks Using an Evidential Methodology Combining with High-Order Analysis
Identifying vital nodes is a basic problem in social network research. The existing theoretical framework mainly focuses on the lower-order structure of node-based and edge-based relations and often ignores important factors such as interactivity and tran
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munications in Computer and Information Science
Data Science 6th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2020 Taiyuan, China, September 18–21, 2020 Proceedings, Part I
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Communications in Computer and Information Science Commenced Publication in 2007 Founding and Former Series Editors: Simone Diniz Junqueira Barbosa, Phoebe Chen, Alfredo Cuzzocrea, Xiaoyong Du, Orhun Kara, Ting Liu, Krishna M. Sivalingam, Dominik Ślęzak, Takashi Washio, Xiaokang Yang, and Junsong Yuan
Editorial Board Members Joaquim Filipe Polytechnic Institute of Setúbal, Setúbal, Portugal Ashish Ghosh Indian Statistical Institute, Kolkata, India Igor Kotenko St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, St. Petersburg, Russia Raquel Oliveira Prates Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil Lizhu Zhou Tsinghua University, Beijing, China
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More information about this series at http://www.springer.com/series/7899
Jianchao Zeng Weipeng Jing Xianhua Song Zeguang Lu (Eds.) •
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Data Science 6th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2020 Taiyuan, China, September 18–21, 2020 Proceedings, Part I
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Editors Jianchao Zeng North University of China Taiyuan, China
Weipeng Jing Northeast Forestry University Harbin, China
Xianhua Song Harbin University of Science and Technology Harbin, China
Zeguang Lu National Academy of Guo Ding Institute of Data Science Beijing, China
ISSN 1865-0929 ISSN 1865-0937 (electronic) Communications in Computer and Information Science ISBN 978-981-15-7980-6 ISBN 978-981-15-7981-3 (eBook) https://doi.org/10.1007/978-981-15-7981-3 © Springer Nature Singapore Pte Ltd. 2020 Chapter “Highly Parallel SPARQL Engine for RDF” is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). For further details see licence information in the chapter. 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 contai
