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Titlebook: Network Science; 7th International Wi Pedro Ribeiro,Fernando Silva,Rosário Laureano Conference proceedings 2022 Springer Nature Switzerland

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發(fā)表于 2025-3-21 20:07:56 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Network Science
副標(biāo)題7th International Wi
編輯Pedro Ribeiro,Fernando Silva,Rosário Laureano
視頻videohttp://file.papertrans.cn/663/662841/662841.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Network Science; 7th International Wi Pedro Ribeiro,Fernando Silva,Rosário Laureano Conference proceedings 2022 Springer Nature Switzerland
描述.This book constitutes the refereed proceedings of the 7.th .International Conference and School of Network Science, NetSci-X 2022, held in Porto, Portugal, in February 2021...The 13 full papers were carefully reviewed and selected from 19 submissions. The papers deal with the study of network models in domains ranging from biology and physics to computer science, from financial markets to cultural integration, and from social media to infectious diseases..
出版日期Conference proceedings 2022
關(guān)鍵詞artificial intelligence; communication networks; communication systems; complex networks; computer netwo
版次1
doihttps://doi.org/10.1007/978-3-030-97240-0
isbn_softcover978-3-030-97239-4
isbn_ebook978-3-030-97240-0Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2022
The information of publication is updating

書目名稱Network Science影響因子(影響力)




書目名稱Network Science影響因子(影響力)學(xué)科排名




書目名稱Network Science網(wǎng)絡(luò)公開度




書目名稱Network Science網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Network Science被引頻次




書目名稱Network Science被引頻次學(xué)科排名




書目名稱Network Science年度引用




書目名稱Network Science年度引用學(xué)科排名




書目名稱Network Science讀者反饋




書目名稱Network Science讀者反饋學(xué)科排名




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Constructing Provably Robust Scale-Free Networks,lizations that are, in fact, provably robust against any vertex removal strategy. We propose an algorithm that constructs such realizations almost surely, requiring only linear time and space. Our experiments confirm the robustness of the networks generated by this algorithm against adaptive and non-adaptive vertex removal strategies.
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Conference proceedings 2022tugal, in February 2021...The 13 full papers were carefully reviewed and selected from 19 submissions. The papers deal with the study of network models in domains ranging from biology and physics to computer science, from financial markets to cultural integration, and from social media to infectious diseases..
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,Deep Topological Embedding with?Convolutional Neural Networks for?Complex Network Classification,x networks. In this sense, we present a convolutional architecture to classify DTE representations of different topological models. Our method achieves improved classification accuracy compared to related methods when tested on three benchmarks.
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