How　to　identify　influential　nodes　is　still　an　open　hot　issue　in　complex　networks.　Lots　of　methods　(e.g.,　degree　centrality,　betweenness　centrality　or　K-shell)　are　based　on　the　topology　of　a　network.　These　methods　work　well　in　scale-free　networks.　In　order　to　design　a　universal　method　suitable　for　networks　with　different　topologies,　this　paper　proposes　a　Multiple　Attribute　Fusion　(MAF)　method　through　combining　topological　attributes　and　diffused　attributes　of　a　node　together.　Two　fusion　strategies　have　been　proposed　in　this　paper.　One　is　based　on　the　attribute　union　(FU),　and　the　other　is　based　on　the　attribute　ranking　(FR).　Simulation　results　in　the　Susceptible-Infected　(SI)　model　show　that　our　proposed　method　gains　more　information　propagation　efficiency　in　different　types　of　networks.　©　2014　Springer　International　Publishing.