Why Are Researchers moving to Leiden community?
Network data contains more than simple connections. Hidden within it are underlying structural patterns. In research collaboration networks, scholars often cluster around specific research topics. On social media, users with similar interests tend to connect more frequently with one another.
In network analysis, these densely connected groups of nodes are called communities. Community detection helps reveal group structures within a network and can be used to understand various phenomena, such as collaboration patterns among individuals, institutions, or countries, the formation of research fields, or clusters of related concepts in text data. Traditionally, methods such as Girvan–Newman community detection and the Louvain method have been widely used.
However, since the Leiden algorithm was introduced by Traag, Waltman, and van Eck in 2019, it has increasingly been adopted in recent network analysis research.
Why are many studies now choosing the Leiden method over earlier approaches? This article briefly introduces the key characteristics of the Leiden algorithm, how it differs from previous methods, and how it is used in practice.