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'''Lancichinetti–Fortunato–Radicchi''' '''benchmark''' is an algorithm that generates [[Benchmark (computing)|benchmark]] networks (artificial networks that resemble real-world networks). They have ''a priori'' known [[Community structure|communities]] and are used to compare different community detection methods.<ref>Hua-Wei Shen (2013). "Community Structure of Complex Networks". Springer Science & Business Media. 11–12.</ref>  The advantage of the benchmark over other methods is that it accounts for the [[Homogeneity (statistics)|heterogeneity]] in the distributions of [[Vertex (graph theory)|node]] [[Degree (graph theory)|degrees]] and of community sizes.

 

 

 

 

'''Lancichinetti–Fortunato–Radicchi''' '''benchmark''' is an algorithm that generates [[Benchmark (computing)|benchmark]] networks (artificial networks that resemble real-world networks). They have ''a priori'' known [[Community structure|communities]] and are used to compare different community detection methods.<ref>Hua-Wei Shen (2013). "Community Structure of Complex Networks". Springer Science & Business Media. 11–12.</ref>  The advantage of the benchmark over other methods is that it accounts for the [[Homogeneity (statistics)|heterogeneity]] in the distributions of [[Vertex (graph theory)|node]] [[Degree (graph theory)|degrees]] and of community sizes.<ref name="original">A. Lancichinetti, S. Fortunato, and F. Radicchi.(2008) Benchmark graphs for testing community detection algorithms. Physical Review E, 78. {{ArXiv|0805.4770}}</ref>

Lancichinetti–Fortunato–Radicchi benchmark is an algorithm that generates benchmark networks (artificial networks that resemble real-world networks). They have a priori known communities and are used to compare different community detection methods.  The advantage of the benchmark over other methods is that it accounts for the heterogeneity in the distributions of node degrees and of community sizes.

Lancichinetti–Fortunato–Radicchi benchmark is an algorithm that generates benchmark networks (artificial networks that resemble real-world networks). They have a priori known communities and are used to compare different community detection methods.  The advantage of the benchmark over other methods is that it accounts for the heterogeneity in the distributions of node degrees and of community sizes.

'''<font color="#ff8000">兰奇基内蒂-福图纳托-拉迪奇基准程序(Lancichinetti–Fortunato–Radicchi benchmark)</font>'''是一种生成'''<font color="#ff8000">基准网络（baseline network)</font>'''(类似于真实世界网络的人工网络)的算法。他们有一个预先已知的社区，用于比较不同的'''<font color="#ff8000">社区检测</font>'''方法。与其他方法相比，基准测试的优点在于它解释了'''<font color="#ff8000">节点度(node degree)</font>'''分布和'''<font color="#ff8000">社区规模(community sizes)</font>'''分布的异质性。

'''<font color="#ff8000">兰奇基内蒂-福图纳托-拉迪奇基准测试 Lancichinetti–Fortunato–Radicchi benchmark</font>'''是一种生成'''基准网络 baseline network'''（类似于真实世界网络的人工网络）的算法。他们有一个预先已知的社区，用于比较不同的社区检测方法。与其他方法相比，基准测试的优点在于它解释了节点度分布和社区规模分布的异质性。<ref name="original">A. Lancichinetti, S. Fortunato, and F. Radicchi.(2008) Benchmark graphs for testing community detection algorithms. Physical Review E, 78. {{ArXiv|0805.4770}}</ref>