An efficient and principled method for detecting communities in networks

Understand

A fundamental problem in the analysis of network data is the detection of network communities, groups of densely interconnected nodes, which may be overlapping or disjoint.

• Here we describe a method for finding overlapping communities based on a principled statistical approach using generative network models.
• We show how the method can be implemented using a fast, closed-form expectation-maximization algorithm that allows us to analyze networks of millions of nodes in reasonable running times.
• We test the method both on real-world networks and on synthetic benchmarks and find that it gives results competitive with previous methods.