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 thisneologism 发表于 2025-3-27 02:02:19
http://reply.papertrans.cn/39/3880/387930/387930_32.png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 “crosBROW 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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http://reply.papertrans.cn/39/3880/387930/387930_37.png含糊其辞 发表于 2025-3-28 04:13:59
http://reply.papertrans.cn/39/3880/387930/387930_38.png议程 发表于 2025-3-28 09:46:42
http://reply.papertrans.cn/39/3880/387930/387930_39.png蒙太奇 发表于 2025-3-28 14:28:36
Synthesis Lectures on Data Mining and Knowledge Discovery387930.jpg