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.