The　mining　and　discovery　of　overlapping　and　hierarchical　communities　is　a　hot　topic　in　the　area　of　social　network　research.　Firstly,　an　algorithm,　discovery　of　link　conmunities　based　on　extended　link　cluster　sequence　(DLC_ECS),　is　proposed　to　detect　overlapping　and　hierarchical　communities　in　social　networks　efficiently.　Based　on　the　extended　link　cluster　sequence　corresponding　to　community　structures　with　various　densities,　the　optimal　link　community　is　detected　after　searching　for　the　global　optimal　density.　The　link　communities　are　transformed　into　the　node　communities,　and　thus　the　overlapping　communities　can　be　found　out.　Then,　hierarchical　link　communities　extraction　based　on　extended　link　cluster　sequence　(HLCE_ECS)　is　designed.　Hierarchical　link　communities　from　the　extended　link　cluster　sequence　is　found　by　the　proposed　algorithm.　The　link　communities　are　transformed　into　the　node　communities　to　find　out　the　overlapping　and　hierarchical　communities.　Experimental　results　on　are　artificial　and　real-world　datasets　demonstrate　that　DLC_ECS　algorithm　significantly　improves　the　community　quality　and　HLCE_ECS　algorithm　effectively　discovers　meaningful　hierarchical　communities.　©,　2015,　Journal　of　Pattern　Recognition　and　Artificial　Intelligence.　All　right　reserved.