A Community Detection Method based on Formal Concept Lattice

Community detection aims to uncover groups of vertices in a network that are more closely connected to each other than to the rest of the graph. While many existing algorithms rely purely on structural information, additional insights can be gained by incorporating conceptual relationships among nodes. This paper presents a community detection framework based on classical concept lattices. The method constructs a lattice from the given context, employs a greedy covering strategy to identify representative communities, and applies a merging mechanism to refine the results. Experiments on synthetic and real-world networks show that the proposed approach produces stable and interpretable communities, demonstrating the potential of concept lattice theory as a tool for network analysis.