注射
发表于 2025-3-21 20:07:56
书目名称Network Science影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0662841<br><br> <br><br>书目名称Network Science影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0662841<br><br> <br><br>书目名称Network Science网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0662841<br><br> <br><br>书目名称Network Science网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0662841<br><br> <br><br>书目名称Network Science被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0662841<br><br> <br><br>书目名称Network Science被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0662841<br><br> <br><br>书目名称Network Science年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0662841<br><br> <br><br>书目名称Network Science年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0662841<br><br> <br><br>书目名称Network Science读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0662841<br><br> <br><br>书目名称Network Science读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0662841<br><br> <br><br>
上流社会
发表于 2025-3-21 20:51:44
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雄辩
发表于 2025-3-22 00:35:09
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.
compel
发表于 2025-3-22 06:53:39
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..
不怕任性
发表于 2025-3-22 12:05:28
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Oscillate
发表于 2025-3-22 13:23:43
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Ischemic-Stroke
发表于 2025-3-22 20:05:57
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AMPLE
发表于 2025-3-23 00:20:16
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过份
发表于 2025-3-23 05:05:56
,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.
deficiency
发表于 2025-3-23 05:56:21
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