Community search in attributed networks poses a dual challenge: balancing structural connectivity -- the network's topological properties -- and attribute similarity -- the shared characteristics of nodes. This paper introduces a novel algorithm that integrates hop-based and random-walk-based methods to identify high-quality communities, effectively addressing this balance. Our approach employs the concept of the domination score to quantify the influence of nodes based on their attributes, followed by $k$-core extraction to ensure strong structural cohesion within the communities. By considering both the network structure and node attributes, the algorithm identifies communities that are not only well-connected, but also share meaningful attribute similarities. We evaluated the algorithm on large real-world datasets, demonstrating its ability to efficiently identify cohesive communities, making it suitable for applications such as social network analysis and recommendation systems.