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Titlebook: Computational Data and Social Networks; 10th International C David Mohaisen,Ruoming Jin Conference proceedings 2021 Springer Nature Switzer

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發(fā)表于 2025-3-21 17:29:57 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Computational Data and Social Networks
副標(biāo)題10th International C
編輯David Mohaisen,Ruoming Jin
視頻videohttp://file.papertrans.cn/233/232227/232227.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Computational Data and Social Networks; 10th International C David Mohaisen,Ruoming Jin Conference proceedings 2021 Springer Nature Switzer
描述This book constitutes the refereed proceedings of the 10th International Conference on Computational Data and Social Networks, CSoNet 2021, which was held online during November 15-17, 2021. The conference was initially planned to take place in Montreal, Quebec, Canada, but changed to an online event due to the COVID-19 pandemic.?.The 24 full and 8 short papers included in this book were carefully reviewed and selected from 57 submissions. They were organized in topical sections as follows: Combinatorial optimization and learning; deep learning and applications to complex and social systems; measurements of insight from data; complex networks analytics; special track on fact-checking, fake news and malware detection in online social networks; and special track on information spread in social and data networks. ..?.
出版日期Conference proceedings 2021
關(guān)鍵詞adversarial attacks of network; artificial intelligence; computational methods for social good; compute
版次1
doihttps://doi.org/10.1007/978-3-030-91434-9
isbn_softcover978-3-030-91433-2
isbn_ebook978-3-030-91434-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2021
The information of publication is updating

書目名稱Computational Data and Social Networks影響因子(影響力)




書目名稱Computational Data and Social Networks影響因子(影響力)學(xué)科排名




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書目名稱Computational Data and Social Networks網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Computational Data and Social Networks被引頻次




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沙發(fā)
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Die beobachteten Eigenschaften der Sterne,dentifiability or unidentifiability of parameters for several special structures including the Markovian IC model, semi-Markovian IC model, and IC model with a global unobserved variable. Parameter identifiability is important for other tasks such as influence maximization under the diffusion networks with unobserved confounding factors.
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Conference proceedings 2021easurements of insight from data; complex networks analytics; special track on fact-checking, fake news and malware detection in online social networks; and special track on information spread in social and data networks. ..?.
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發(fā)表于 2025-3-22 14:11:18 | 只看該作者
0302-9743 systems; measurements of insight from data; complex networks analytics; special track on fact-checking, fake news and malware detection in online social networks; and special track on information spread in social and data networks. ..?.978-3-030-91433-2978-3-030-91434-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
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發(fā)表于 2025-3-22 17:34:30 | 只看該作者
Sterne G?tter, Mensch und Mythenhe edges with negative labels within clusters. In this paper, we propose the lower bounded correlation clustering problem and formulate the problem as an integer program. Furthermore, we provide two polynomial time algorithms with constant approximate ratios for the lower bounded correlation clustering problem on some special graphs.
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https://doi.org/10.1007/978-3-7091-6773-1algorithm, which implements the . approximation of the maximizing unconstrained non-negative weakly-monotone function problem. When ., the algorithm achieves an approximate guarantee of 1/3, achieving the same ratio as the deterministic algorithm for the unconstrained submodular maximization problem.
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