Community detection on attributed networks has gained attention due to its ability to reveal complex relationships by considering topological and attributed features. Real-world networks generally have multiple attributes leading to overlapping memberships across communities. This paper proposes an attributed overlapping community detection (AttrOverlapComm) algorithm that can effectively detect overlapping communities by combining the information from node attributes and the network topology. This problem is relevant as it has applications in various domains, including social network analysis and recommendation systems. The experiments are conducted on real-world datasets to validate the proposed approach’s effectiveness across diverse network structures and attribute characteristics.