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→Barabási–Albert (BA) preferential attachment model
=== Barabási–Albert (BA) preferential attachment model ===
=== Barabási–Albert (BA) preferential attachment model ===
The [[Barabási–Albert model]] is a random network model used to demonstrate a preferential attachment or a "rich-get-richer" effect. In this model, an edge is most likely to attach to nodes with higher degrees.
The [[Barabási–Albert model]] is a random network model used to demonstrate a preferential attachment or a "rich-get-richer" effect. In this model, an edge is most likely to attach to nodes with higher degrees.The network begins with an initial network of ''m''<sub>0</sub> nodes. ''m''<sub>0</sub> ≥ 2 and the degree of each node in the initial network should be at least 1, otherwise it will always remain disconnected from the rest of the network.
The network begins with an initial network of ''m''<sub>0</sub> nodes. ''m''<sub>0</sub> ≥ 2 and the degree of each node in the initial network should be at least 1, otherwise it will always remain disconnected from the rest of the network.
The Barabási–Albert model is a random network model used to demonstrate a preferential attachment or a "rich-get-richer" effect. In this model, an edge is most likely to attach to nodes with higher degrees. The network begins with an initial network of m0 nodes. m0 ≥ 2 and the degree of each node in the initial network should be at least 1, otherwise it will always remain disconnected from the rest of the network.
=== Barabási–Albert (BA) 优先链接模型 ===
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 m0节点的初始网络开始。 M0≥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。
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''<sub>''i''</sub> that the new node is connected to node ''i'' is<ref name=RMP>{{Cite journal
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''<sub>''i''</sub> that the new node is connected to node ''i'' is<ref name=RMP>{{Cite journal