Computational Data And Social Networks


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Computational Data and Social Networks


Computational Data and Social Networks

Author: Minh Hoàng Hà

language: en

Publisher: Springer Nature

Release Date: 2024-02-28


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This book constitutes the refereed conference proceedings of the 12th International Conference on Computational Data and Social Networks, CSoNet 2023, held in Hanoi, Vietnam, in December 2023. The 23 full papers and 14 short papers presented in this book were carefully reviewed and selected from 64 submissions. The papers are divided into the following topical sections: machine learning and prediction; optimization; security and blockchain; and network analysis.

Computational Data and Social Networks


Computational Data and Social Networks

Author: Xuemin Chen

language: en

Publisher: Springer

Release Date: 2018-12-11


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This book constitutes the refereed proceedings of the 7th International Conference on Computational Data and Social Networks, CSoNet 2018, held in Shanghai, China, in December 2018. The 44 revised full papers presented in this book toghether with 2 extended abstracts, were carefully reviewed and selected from 106 submissions. The topics cover the fundamental background, theoretical technology development, and real-world applications associated with complex and data network analysis, minimizing in uence of rumors on social networks, blockchain Markov modelling, fraud detection, data mining, internet of things (IoT), internet of vehicles (IoV), and others.

Computational Data and Social Networks


Computational Data and Social Networks

Author: Andrea Tagarelli

language: en

Publisher: Springer Nature

Release Date: 2019-11-11


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This book constitutes the refereed proceedings of the 8th International Conference on Computational Data and Social Networks, CSoNet 2019, held in Ho Chi Minh City, Vietnam, in November 2019. The 22 full and 8 short papers presented in this book were carefully reviewed and selected from 120 submissions. The papers appear under the following topical headings: Combinatorial Optimization and Learning; Influence Modeling, Propagation, and Maximization; NLP and Affective Computing; Computational Methods for Social Good; and User Profiling and Behavior Modeling.