The　time　complexity　of　multi-label　propagation　algorithm　(MLPA)　is　nearly　linear.　However,　when　it　is　applied　to　overlapping　community　discovery,　the　accuracy　and　the　stability　of　MLPA　are　poor.　Inspired　by　the　idea　that　overlapping　nodes　are　more　probable　to　appear　in　the　boundary　regions　of　different　communities,　an　overlapping　community　discovery　algorithm　based　on　node　hierarchy　and　label　propagation　gain　is　proposed　in　this　paper.　Firstly,　the　improved　single　label　propagation　with　node　centrality　and　community　distribution　constraints　is　utilized　to　unfold　preliminary　non-overlapping　communities　and　centrality　values　of　nodes　are　calculated　by　local　information　in　the　propagation　process　simultaneously.　Furthermore,　node　hierarchy　partition　function　is　defined　according　to　centrality　values　of　nodes　and　employed　to　mark　the　hierarchy　of　each　node　in　its　respective　community.　Finally,　based　on　the　label　propagation　gain　among　nodes,　a　new　multi-label　updating　rule　is　designed　to　obtain　the　final　overlapping　communities.　Extensive　experimental　results　on　synthetic　and　real-world　networks　validate　that　the　proposed　algorithm　effectively　improves　the　accuracy　and　stability.　©,　2015,　Journal　of　Pattern　Recognition　and　Artificial　Intelligence.　All　right　reserved.