Community　detection　in　social　networks　is　usually　considered　as　an　objective　optimization　problem.　Due　to　the　limitation　of　the　objective　function,　the　global　optimum　cannot　describe　the　real　partition　well,　and　it　is　time　consuming.　In　this　paper,　a　novel　PSO　(particle　swarm　optimization)　algorithm　based　on　modularity　optimization　for　community　detection　in　social　networks　is　proposed.　Firstly,　the　algorithm　takes　similarity-based　clustering　to　find　core　areas　in　the　network,　and　then　a　modified　particle　swarm　optimization　is　performed　to　optimize　modularity　in　a　new　constructed　weighted　network　which　is　compressed　from　the　original　one,　and　it　is　equivalent　to　optimize　modularity　in　the　original　network　with　some　restriction.　Experiments　are　conducted　in　the　synthetic　and　four　real-world　networks.　The　experimental　results　show　that　the　proposed　algorithm　can　effectively　extract　the　intrinsic　community　structures　of　social　networks.　©　Springer-Verlag　Berlin　Heidelberg　2013.