Many　works　have　been　done　to　identify　functional　modules　in　Protein-Protein　Interaction　(PPI)　networks　but　the　results　are　far　from　satisfaction.　One　main　reason　is　that　the　PPI　data　generated　from　high-throughput　experiments　is　noisy　and　incomplete.　Solving　the　problem　goes　beyond　what　a　single　data　source　can　provide　and　thus　requires　the　integration　of　multiple　information　sources.　To　address　this　problem,　we　hereby　propose　a　graph-based　cluster　ensemble　method　which　integrates　gene　ontology　(GO)　and　gene　expression　data　with　PPI　networks.　Experimental　results　show　that　our　method　is　superior　to　the　baseline　methods　and　demonstrate　the　benefits　of　integrating　multiple　biological　information　sources　and　diverse　clustering　methods.