The document summarizes research on detecting communities in the DBLP bibliographic network. It defines a community as a densely connected group of nodes sparsely connected to outside nodes. Community detection aims to identify these groups and their structure. The researchers used the DBLP dataset and constructed graphs based on co-authorship, closeness between authors, and uniform edge weights. They applied the Louvain algorithm to detect communities and calculated modularity to evaluate community quality. The co-authorship and closeness graphs detected over 40,000 communities with modularity over 0.8, while the uniform edge weight graph found over 21,000 communities with the highest modularity of 0.94646.