Detecting meaningful structures such as communities
One common problem data scientists face when dealing with networks is how to identify clusters and communities within a graph. This often arises when graphs are derived from social networks and communities are known to exist. However, the underlying algorithms and methods can also be used in other contexts, representing another option to perform clustering and segmentation. For example, these methods can effectively be used in text mining to identify emerging topics and to cluster documents that refer to single events/topics. A community detection task consists of partitioning a graph such that nodes belonging to the same community are tightly connected with each other and are weakly connected with nodes from other communities. There exist several strategies to identify communities. In general, we can define them as belonging to one of two categories, outlined as follows:
- Non-overlapping community detection algorithms that...
