Community detection is a fundamental problem in complex network analysis that aims to find closely related groups of nodes. Recently, network embedding techniques have been integrated into community detection in two manners to capture the intricate relationships between nodes. The two-staged manner generates node embedding vectors and obtains communities by running a clustering algorithm on them. The single-staged manner simultaneously obtains node embedding vectors and communities by optimizing a hybrid objective concerning with node–community relationships. The general-purpose network embedding algorithms used in the first manner do not emphasize retaining node–community relationships. The second manner ignores the influence of a node’s location in a community (at the center or boundary) and its attributes on community generation. In this article, we propose a biased-random-walk-based community detection (BRWCD) algorithm to tackle the issues. First, a topology-weighted degree is designed to enhance the random walk at the boundary of and inside a community to extract communities precisely. Second, we design an attribute-to-node influence index and an attribute-weighted degree to distinguish different attributes’ influence on node transition to obtain communities with high internal cohesion. Comprehensive experiments on the real-world and synthetic networks demonstrate that BRWCD achieves nearly 10% higher accuracy at most than the state-of-the-art algorithms.