Efficiently　mapping　multiple　independent　Virtual　networks　(VNs)　over　a　common　infrastructure　substrate　is　a　challenging　problem　on　cloud　computing　platforms　and　large-scale　future　Internet　testbeds.　Inspired　by　the　idea　of　data　fields,　we　apply　a　topological　potential　function　to　node　ranking　and　propose　an　algorithm　called　Locality-aware　node　topological　potential　ranking　(LNTPR),　which　can　precisely　and　efficiently　reflect　the　relative　importance　of　nodes.　Using　LNTPR　and　the　concept　of　locality　awareness,　we　develop　the　Locality-aware　influence　choosing　node　(LICN)　algorithm　based　on　a　node　influence　model　that　considers　the　mutual　influence　between　a　mapped　node　and　its　candidate　mapping　nodes.　LNTPR　and　LICN　improve　the　integration　of　node　and　link　mapping.　Simulation　results　demonstrate　that　the　proposed　algorithms　exhibit　good　performance　in　determining　revenue,　acceptance　ratio,　and　revenue/cost　ratio.