Most　classic　network　entity　sorting　algorithms　are　implemented　in　a　homogeneous　network,　and　they　are　not　applicable　to　a　heterogeneous　network.　Registered　patent　history　data　denotes　the　innovations　and　the　achievements　in　different　research　fields.　In　this　paper,　we　present　an　iteration　algorithm　called　inventor-ranking,　to　sort　the　influences　of　patent　inventors　in　heterogeneous　networks　constructed　based　on　their　patent　data.　This　approach　is　a　flexible　rule-based　method,　making　full　use　of　the　features　of　network　topology.　We　sort　the　inventors　and　patents　by　a　set　of　rules,　and　the　algorithm　iterates　continuously　until　it　meets　a　certain　convergence　condition.　We　also　give　a　detailed　analysis　of　influential　inventor’s　interesting　topics　using　a　latent　Dirichlet　allocation　(LDA)　model.　Compared　with　the　traditional　methods　such　as　PageRank,　our　approach　takes　full　advantage　of　the　information　in　the　heterogeneous　network,　including　the　relationship　between　inventors　and　the　relationship　between　the　inventor　and　the　patent.　Experimental　results　show　that　our　method　can　effectively　identify　the　inventors　with　high　influence　in　patent　data,　and　that　it　converges　faster　than　PageRank.　©　2015,　Journal　of　Zhejiang　University　Science　Editorial　Office　and　Springer-Verlag　Berlin　Heidelberg.