Detecting　functional　modules　from　a　Protein-Protein　Interaction　(PPI)　network　is　a　fundamental　and　hot　issue　in　proteomics　research,　where　many　computational　approaches　have　played　an　important　role　in　recent　years.　However,　how　to　effectively　and　efficiently　detect　functional　modules　in　large-scale　PPI　networks　is　still　a　challenging　problem.　We　present　a　new　framework,　based　on　a　multiple-grain　model　of　PPI　networks,　to　detect　functional　modules　in　PPI　networks.　First,　we　give　a　multiple-grain　representation　model　of　a　PPI　network,　which　has　a　smaller　scale　with　super　nodes.　Next,　we　design　the　protein　grain　partitioning　method,　which　employs　a　functional　similarity　or　a　structural　similarity　to　merge　some　proteins　layer　by　layer.　Thirdly,　a　refining　mechanism　with　border　node　tests　is　proposed　to　address　the　protein　overlapping　of　different　modules　during　the　grain　eliminating　process.　Finally,　systematic　experiments　are　conducted　on　five　large-scale　yeast　and　human　networks.　The　results　show　that　the　framework　not　only　significantly　reduces　the　running　time　of　functional　module　detection,　but　also　effectively　identifies　overlapping　modules　while　keeping　some　competitive　performances,　thus　it　is　highly　competent　to　detect　functional　modules　in　large-scale　PPI　networks.