Скачать или смотреть Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 14.4 - Kronecker Graph Model

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Jure Leskovec
Computer Science, PhD

We introduce the Kronecker Graph model, where graphs are generated in a recursive manner. The key motivation is that real-world graphs often exhibit self-similarity, where the whole structure of the graph has the same shape as its parts. Kronecker graphs are generated by recursively doing Kronecker product over the initiator matrix, which is trained to fit the statistics of the input dataset. We further discuss fast Kronecker generator algorithms. Finally, we show that Kronecker graphs and real graphs are very close in many important graph statistics.

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