As summer winds down, many of us in continental Europe are heading back north. The long return journeys from the beaches of southern France, Spain, and Italy once again clog alpine tunnels and Mediterranean coastal routes during the infamous Black Saturday bottlenecks. This annual migration, like many systems in our world, forms a network—not just of connections, but of communities shaped by shared patterns of origin and destination.

This is where network science —and in particular, community detection—comes in. For decades, researchers have developed powerful tools to uncover communities in networks: clusters of tightly interconnected nodes. But these tools work best for undirected networks, where connections are mutual. Graphically, the node maps may look familiar.

These clusters can mean that a group of people are all friends on Facebook, follow different sport accounts on X, or all live in the same city. Using a standard modularity algorithm, we can then find connections between different communities and begin to draw useful conclusions. Perhaps users in the fly-fishing community also show up as followers of nonalcoholic beer enthusiasts in Geneva. This type of information extraction, impossible without community analysis, is a layer of meaning that can be leveraged to sell beer or even nefariously influence elections.