Community detection in multi-relational social networks based on relational concept analysis

Multi-relational community discovering in heterogeneous social networks is an important issue. Many approaches has been proposed for community discovering in heterogeneous networks. However, they have focused only on topological properties of these networks, ignoring the embedded semantic information. As the solution to this information glut limit, we propose, in this paper, a new multi-relational community discovering approach which incorporates the multiple types of objects and relationships, derived from heterogeneous networks. Firstly, we propose to construct the Concept Lattice Family CLF to represent the different objects and relations of the heterogeneous social networks based on the relational concept analysis techniques. Then after we introduce a new algorithm that explores such CLF and extract the multi-relational communities. Carried out experiments on real-datasets enhance the effectiveness of our proposal and open promising issues. (C) 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of KES International.