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[[BA模型]]是一个随机网络模型，用于说明偏好依附效应（优先链接）preferential attachment或“富人越富”效应。 在这个模型中，边最有可能附着在度数较高的节点上。 这个网络从一个 ''m''₀节点的初始网络开始。 ''m''₀ ≥ 2，初始网络中每个节点的度至少为 1，否则它将始终与网络的其余部分断开。

[[BA模型]]是一个随机网络模型，用于说明偏好依附效应（优先链接）preferential attachment或“富人越富”效应。 在这个模型中，边最有可能附着在度数较高的节点上。 这个网络从一个 ''m''₀节点的初始网络开始。 ''m''₀ ≥ 2，初始网络中每个节点的度至少为 1，否则它将始终与网络的其余部分断开。

在BA模型中，每次向网络中添加一个新节点。每个新节点和已有的<math>m</math>个节点相连接，连接的概率正比于每个已存在节点当前的度。In the BA model, new nodes are added to the network one at a time. Each new node is connected to <math>m</math> existing nodes with a probability that is proportional to the number of links that the existing nodes already have. Formally, the probability ''p''_''i'' that the new node is connected to node ''i'' is<ref name=RMP>{{Cite journal

在BA模型中，每次向网络中添加一个新节点。每个新节点和已有的<math>m</math>个节点相连接，连接的概率正比于每个已存在节点当前的度。形式上，新节点与节点''i''相连的概率''p''_''i''为<ref name=RMP>{{Cite journal

  |url        = http://www.nd.edu/~networks/Publication%20Categories/03%20Journal%20Articles/Physics/StatisticalMechanics_Rev%20of%20Modern%20Physics%2074,%2047%20(2002).pdf

  |url        = http://www.nd.edu/~networks/Publication%20Categories/03%20Journal%20Articles/Physics/StatisticalMechanics_Rev%20of%20Modern%20Physics%2074,%2047%20(2002).pdf

  |author1    = R. Albert

  |author1    = R. Albert

: <math>p_i = \frac{k_i}{\sum_j k_j},</math>

: <math>p_i = \frac{k_i}{\sum_j k_j},</math>

where ''k''_''i'' is the degree of node ''i''. Heavily linked nodes ("hubs") tend to quickly accumulate even more links, while nodes with only a few links are unlikely to be chosen as the destination for a new link. The new nodes have a "preference" to attach themselves to the already heavily linked nodes.

其中 ''k''_''i'' 是节点''i''的度。大量连接的节点(“中心”)倾向于快速累积更多的连接，而只有少量连接的节点不太可能被选为新连接的目的地。新节点“偏好”将自己附加到已经高度连接的节点上。

[[File:Barabasi-albert model degree distribution.svg|thumb|The degree distribution of the BA Model, which follows a power law. In loglog scale the power law function is a straight line.<ref name=Barabasi1999>{{Cite journal

[[File:Barabasi-albert model degree distribution.svg|thumb|The degree distribution of the BA Model, which follows a power law. In loglog scale the power law function is a straight line.<ref name=Barabasi1999>{{Cite journal