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WS模型是针对于第一条局限性来设计的最简可能模型。它在解释了集聚的同时保持了ER模型较短的平均节点间距离，这是通过在近似ER图的随机化结构和正则环[https://en.wikipedia.org/wiki/Lattice_(group) 点阵 regular ring lattice]中进行内插得到的。因而，该模型至少能够部分解释许多网络中的“小世界”现象 "small-world" phenomena，比如电网、秀丽隐杆线虫 C. elegans的神经网络、电影演员的社交网络、以及芽殖酵母脂肪代谢的信息交流

WS模型是针对于第一条局限性来设计的最简可能模型。它在解释了集聚的同时保持了ER模型较短的平均节点间距离，这是通过在近似ER图的随机化结构和[https://en.wikipedia.org/wiki/Lattice_(group) 正则环点阵 regular ring lattice]中进行内插得到的。因而，该模型至少能够部分解释许多网络中的“小世界”现象 "small-world" phenomena，比如电网、秀丽隐杆线虫 C. elegans的神经网络、电影演员的社交网络、以及芽殖酵母脂肪代谢的信息交流

<ref>{{cite journal |doi=10.1371/journal.pcbi.1004264|pmid=26020510|title=Experimental and Computational Analysis of a Large Protein Network That Controls Fat Storage Reveals the Design Principles of a Signaling Network|journal=PLOS Computational Biology|volume=11|issue=5|pages=e1004264|year=2015|last1=Al-Anzi|first1=Bader|last2=Arpp|first2=Patrick|last3=Gerges|first3=Sherif|last4=Ormerod|first4=Christopher|last5=Olsman|first5=Noah|last6=Zinn|first6=Kai|bibcode=2015PLSCB..11E4264A|pmc=4447291}}</ref>

<ref>{{cite journal |doi=10.1371/journal.pcbi.1004264|pmid=26020510|title=Experimental and Computational Analysis of a Large Protein Network That Controls Fat Storage Reveals the Design Principles of a Signaling Network|journal=PLOS Computational Biology|volume=11|issue=5|pages=e1004264|year=2015|last1=Al-Anzi|first1=Bader|last2=Arpp|first2=Patrick|last3=Gerges|first3=Sherif|last4=Ormerod|first4=Christopher|last5=Olsman|first5=Noah|last6=Zinn|first6=Kai|bibcode=2015PLSCB..11E4264A|pmc=4447291}}</ref>

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