Titlebook: ;

有抱负者

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

残酷的地方

https://doi.org/10.1007/978-3-322-92875-7s believed to exist) in many real-world graphs, especially social networks: Moody [212] finds groupings based on race and age in a network of friendships in one American school;, Schwartz and Wood [244] group people with shared interests from email logs; Borgs et al. [57] find communities from “cros

BROW

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

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

抛物线

含糊其辞

议程

蒙太奇

Synthesis Lectures on Data Mining and Knowledge Discovery387930.jpg