Community　detection　plays　an　important　role　in　the　complex　networks.　Modularity　is　the　most　widely　used　measure　of　the　partition　quality.　In　this　paper,　we　present　a　community　detection　method　for　optimizing　the　modularity.　We　use　the　idea　of　multilevel　paradigm　to　improve　the　state-of-the-art　detection　algorithm　BGLL.　Our　method　contains　three　phases:　cluster　phase,　modularity　optimization　phase,　and　refinement　phase.　Experimental　results　on　a　set　of　well-known　benchmark　networks　show　that,　our　method　is　efficient　and　can　obtain　equal　or　greater　max　modularity　for　all　tested　benchmark　networks.　1553-9105/Copyright　©　2014　Binary　Information　Press.