The　Network　representation　learning　methods　based　on　random　walk　aim　to　learn　a　low-dimensional　embedding　vector　for　each　node　in　a　network　by　randomly　traversing　the　network　to　capture　the　features　of　nodes　and　edges,　which　is　beneficial　to　many　downstream　machine　learning　tasks　such　as　community　detection.　Most　of　the　existing　random-walk-based　network　representation　learning　algorithms　emphasize　the　neighborhood　of　nodes　but　ignore　the　communities　they　may　form　and　apply　the　same　random　walk　strategy　to　all　nodes　without　distinguishing　the　characteristics　of　different　nodes.　In　addition,　it　is　time-consuming　to　determine　the　most　suitable　random　walk　parameters　for　a　given　network.　In　this　paper,　we　propose　a　novel　overlapping　community　detection　algorithm　based　on　network　representation　learning　which　integrates　community　information　into　embedding　vectors　to　improve　the　cohesion　degree　of　similar　nodes　in　the　embedding　space.　First,　a　node-centrality-based　walk　strategy　is　designed　to　determine　the　parameters　of　random　walk　automatically　to　avoid　the　time-consuming　manual　selection.　Second,　two　community-aware　random　walk　strategies　for　high　and　low　degree　nodes　are　developed　to　capture　the　characteristics　of　the　community　centers　and　boundaries.　The　experimental　results　on　the　synthesized　and　real-world　datasets　demonstrate　the　effectiveness　and　efficiency　of　our　algorithm　on　overlapping　community　detection　compared　with　the　state-of-the-art　algorithms