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Motivated by widely observed examples in nature, society and software, where groups of related nodes arrive together and attach to existing networks, the authors consider network growth via sequential attachment of linked node groups or graphlets. They introduce a fifteen-dimensional attribute vector derived from seven well-known network properties, which enables comprehensive comparison between any two networks. Principal Component Analysis of this attribute vector space shows a significantly larger coverage potential of real-world network properties by a simple extension of the above model when compared against a classic model of network growth.
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