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A Deterministic Complex Network Model: generate modules spontaneously and keep splitting

Unlike a complex network model based on probability, we assign two weights to the nodes in the network, the nodes search for a relatively optimal connection through their neighbors according to fixed rules. There are no requirements for generating modules in the rules, but with the addition of nodes, the network spontaneously generates locally dense connections, and the corresponding modules will also spontaneously split. We show that the average path length between nodes in this network is approximately log2(ln(N)), where N is the number of network nodes, this result is similar to the data of Facebook's social network. The brain network also has the property of multiple modules, during the growth of the brain, existing modules will also split into sub-modules. Our model shows that genes can control the growth of the brain with relatively little information. It is also recommended that fully distributed computer networks can be built through this algorithm.