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* (b) A new node in a scale-free network has a tendency to link to a node with a higher degree, compared to a new node in a random network which links itself to a random node. This process is called [[preferential attachment]]. The tendency of a new node to link to a node with a high degree ''k'' is characterized by [[Power law|power-law distribution]] (also known as rich-gets-richer process). This idea was introduced by [[Vilfredo Pareto]] and it explained why a small percentage of the population earns most of the money. This process is present in networks as well, for example 80 percent of web links point to 15 percent of webpages. The emergence of scale-free networks is not typical only of networks created by human action, but also of such networks as metabolic networks or illness networks.<ref>Barabási, Albert-László. ''Network Science: The Scale-Free Property''., p. 8.[http://barabasi.com/networksciencebook/content/book_chapter_2.pdf]</ref> This phenomenon may be explained by the example of hubs on the World Wide Web such as Facebook or Google. These webpages are very well known and therefore the tendency of other webpages pointing to them is much higher than linking to random small webpages.

* (b) A new node in a scale-free network has a tendency to link to a node with a higher degree, compared to a new node in a random network which links itself to a random node. This process is called [[preferential attachment]]. The tendency of a new node to link to a node with a high degree ''k'' is characterized by [[Power law|power-law distribution]] (also known as rich-gets-richer process). This idea was introduced by [[Vilfredo Pareto]] and it explained why a small percentage of the population earns most of the money. This process is present in networks as well, for example 80 percent of web links point to 15 percent of webpages. The emergence of scale-free networks is not typical only of networks created by human action, but also of such networks as metabolic networks or illness networks.<ref>Barabási, Albert-László. ''Network Science: The Scale-Free Property''., p. 8.[http://barabasi.com/networksciencebook/content/book_chapter_2.pdf]</ref> This phenomenon may be explained by the example of hubs on the World Wide Web such as Facebook or Google. These webpages are very well known and therefore the tendency of other webpages pointing to them is much higher than linking to random small webpages.

*（b）无标度网络中的新节点倾向于链接到度较高的节点，而随机网络中的新节点则与其他节点随机相连。这一过程称为优先链接。新节点链接到度''k''较高节点的倾向可可以用幂律分布来刻画(即，富者越富)。这一想法由'''<font color="#ff8000">维尔弗雷多·帕累托 Vilfredo Pareto</font>'''提出，它解释了为什么一小部分人赚了大部分的钱。这个过程也存在于网络中，例如80%的网络链接指向15%的网页。无标度网络的出现并不仅仅是人类行为所创造的网络的典型现象，在新陈代谢网络或疾病网络中这一现象也十分常见。<ref>Barabási, Albert-László. ''Network Science: The Scale-Free Property''., p. 8.[http://barabasi.com/networksciencebook/content/book_chapter_2.pdf]</ref> 这种现象可以由像Facebook或谷歌这样的万维网枢纽的来解释。这些网页是非常有名的，因此相较于指向随机的小网页，其他网页指向他们的倾向要高得多。

*（b）无标度网络中的新节点倾向于链接到度较高的节点，而随机网络中的新节点则与其他节点随机相连。这一过程称为优先链接。新节点链接到度''k''较高节点的倾向可以用幂律分布来刻画(即，富者越富)。这一想法由'''<font color="#ff8000">维尔弗雷多·帕累托 Vilfredo Pareto</font>'''提出，它解释了为什么一小部分人赚了大部分的钱。这个过程也存在于网络中，例如80%的网络链接指向15%的网页。无标度网络的出现并不仅仅是人类行为所创造的网络的典型现象，在新陈代谢网络或疾病网络中这一现象也十分常见。<ref>Barabási, Albert-László. ''Network Science: The Scale-Free Property''., p. 8.[http://barabasi.com/networksciencebook/content/book_chapter_2.pdf]</ref> 这种现象可以由像Facebook或谷歌这样的万维网枢纽的来解释。这些网页是非常有名的，因此相较于指向随机的小网页，其他网页指向他们的倾向要高得多。

The mathematical explanation for [[Barabási–Albert model]]:

The mathematical explanation for [[Barabási–Albert model]]: