有抱负者 发表于 2025-3-26 21:02:31

https://doi.org/10.1007/978-3-642-93257-1o all the others. There is also the question of how to fit model parameters to match a given graph. What we would like is a tradeoff between parsimony (few model parameters), realism (matching most graph patterns, if not all), and efficiency (in parameter fitting and graph generation speed). In this

neologism 发表于 2025-3-27 02:02:19

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残酷的地方 发表于 2025-3-27 08:42:30

https://doi.org/10.1007/978-3-322-92875-7s believed to exist) in many real-world graphs, especially social networks: Moody finds groupings based on race and age in a network of friendships in one American school;, Schwartz and Wood group people with shared interests from email logs; Borgs et al. find communities from “cros

BROW 发表于 2025-3-27 13:00:08

https://doi.org/10.1007/978-3-642-93257-1 (few model parameters), realism (matching most graph patterns, if not all), and efficiency (in parameter fitting and graph generation speed). In this section, we present the . generator, which attempts to address all of these concerns.

inspiration 发表于 2025-3-27 15:13:08

https://doi.org/10.1007/978-3-8348-9084-9 small initial matrix is recursively “multiplied” with itself to yield large graph topologies. The mathematical simplicity of this generative model yields simple closed-form expressions for several measures of interest, such as degree distributions and diameters, thus enabling ease of analysis and parameter-fitting.

六个才偏离 发表于 2025-3-27 20:14:55

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抛物线 发表于 2025-3-27 22:43:37

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含糊其辞 发表于 2025-3-28 04:13:59

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议程 发表于 2025-3-28 09:46:42

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蒙太奇 发表于 2025-3-28 14:28:36

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