Detecting　functional　modules　from　protein-protein　interaction　(PPI)　networks　is　an　active　research　area　with　many　practical　applications.　However,　there　is　always　a　critical　concern　on　the　false　PPI　interactions　which　are　derived　from　the　high-throughput　experiments　and　the　unsatisfactory　results　obtained　from　single　PPI　network　with　severe　information　insufficiency.　To　address　this　problem,　we　propose　a　Collective　Non-negative　Matrix　Factorization　(CoNMF)　based　soft　clustering　method　which　efficiently　integrates　information　of　gene　ontology　(GO),　gene　expression　data　and　PPI　networks.　In　our　method,　the　three　data　sources　are　formed　into　two　graphs　with　similarity　adjacency　matrices　and　these　graphs　are　approximated　by　a　matrix　factorization　with　their　common　factor　which　provides　the　straightforward　interpretation　of　clustering　results.　Extensive　experiments　show　that　we　can　improve　the　module　detection　performance　by　integrating　multiple　biological　data　sources　and　that　CoNMF　yields　superior　results　compared　to　other　multiple　data　sources　fusion　methods　by　identifying　a　larger　number　of　more　precise　protein　modules　with　actual　biological　meaning　and　certain　degree　of　overlapping.　Copyright　©　2012　ACM.