Community Detection

In the last two chapters, we covered whole network analysis and egocentric network analysis. The former was useful for understanding the complete makeup of a complex network. The latter was useful for investigating the people and relationships that exist around an “ego” node. However, there’s a missing layer that we have not yet discussed. Between whole networks and egos, communities exist. We are people, and we are part of a global population of humans that exist on this planet, but we are each also part of individual communities. For instance, we work in companies and as part of individual teams. Many of us have social interests, and we know people from participating in activities. There are layers to life, and we can use algorithms to identify the various communities that exist in a network, automatically.

This chapter contains the following sections:

• Introducing community detection
• Getting started with community detection
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