不安
发表于 2025-3-23 10:46:03
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frozen-shoulder
发表于 2025-3-23 15:14:28
https://doi.org/10.1007/978-3-030-13712-0 community detection. Existing algorithms, including the popular modularity-optimization methods, look for regions of the network that are better connected internally, e.g., have higher than expected number of edges within them. We believe, however, that edges do not give the true measure of network
Arboreal
发表于 2025-3-23 21:52:16
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填满
发表于 2025-3-23 23:17:39
Recognizing Chords with EDS: Part One,n the context of the spread of a disease or undesirable behavior, it is important to identify .: individuals that are most effective in stopping or slowing down the spread of a process through the population. This problem has so far resisted systematic algorithmic solutions. In an effort to formulat
赤字
发表于 2025-3-24 02:32:06
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无礼回复
发表于 2025-3-24 07:15:42
Mika Kuuskankare,Mikael Laurson work studies them in isolation. Here, we investigate how these networks can be overlaid, and propose a feature taxonomy for link prediction. We show that when there are tightly-knit family circles in a social network, we can improve the accuracy of link prediction models. This is done by making use
蚀刻
发表于 2025-3-24 13:14:58
https://doi.org/10.1007/978-3-540-85035-9portant limitations of modularity as a community quality criterion: a resolution limit; and a bias towards finding equal-sized communities. Information-theoretic approaches that search for partitions that minimize description length are a recent alternative to modularity. This paper shows that two i
cataract
发表于 2025-3-24 16:23:02
https://doi.org/10.1007/978-3-642-14929-0blog networks; classification; community detection; computer networks; data mining; social networks
Benign
发表于 2025-3-24 22:56:18
978-3-642-14928-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer-Verlag GmbH, DE
轻快走过
发表于 2025-3-24 23:59:38
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