Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 13.2 – Network Communities

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Jure Leskovec
Computer Science, PhD

We introduce methods that build on the intuitions presented in the previous part to identify clusters within networks. We define modularity score Q that measures how well a network is partitioned into communities. We also introduce null models to measure expected number of edges between nodes to compute the score. Using this idea, we then give a mathematical expression to calculate the modularity score. Finally, we can develop an algorithm to find communities by maximizing the modularity.

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