Community　detection　is　a　significant　research　direction　in　the　research　of　social　networks.　To　improve　the　quality　of　seeds　selection　and　expansion,　we　propose　an　influence　seeds　extension　overlapping　community　detection　(i-SEOCD)　algorithm　for　overlapping　community　detection.　First,　i-SEOCD　uses　a　node　influence　strategy　to　find　the　seed　communities　with　tight　structures.　Second,　on　the　basis　of　the　seed　communities,　we　calculate　the　similarity　among　communities　and　their　neighbor　nodes.　The　nodes　whose　similarity　is　greater　than　a　predefined　threshold　are　selected.　Third,　the　strategy　of　optimizing　a　self-adaptive　function　is　adopted　to　expand　the　communities.　Finally,　the　free　nodes　in　the　network　are　assigned　to　their　corresponding　communities　in　order　to　find　out　all　the　overlapping　community　structures.　Experiments　on　the　real　and　artificial　networks　show　that　i-SEOCD　is　capable　of　discovering　overlapping　communities　in　complex　social　networks　efficiently.　©　2019,　Chinese　Institute　of　Electronics.　All　right　reserved.