document: Microscopic evolution of social networks We present a detailed study of network evolution by analyzing four large online social networks with full temporal information about node and edge arrivals . For the first time at such a large scale , we study individual node arrival and edge creation processes that collectively lead to macroscopic properties of networks . Using a methodology based on the maximum-likelihood principle , we investigate a wide variety of network formation strategies , and show that edge locality plays a critical role in evolution of networks . Our findings supplement earlier network models based on the inherently non-local preferential attachment . Based on our observations , we develop a complete model of network evolution , where nodes arrive at a prespecified rate and select their lifetimes . Each node then independently initiates edges according to a `` gap '' process , selecting a destination for each edge according to a simple triangle-closing model free of any parameters . We show analytically that the combination of the gap distribution with the node lifetime leads to a power law out-degree distribution that accurately reflects the true network in all four cases . Finally , we give model parameter settings that allow automatic evolution and generation of realistic synthetic networks of arbitrary scale .
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