Neural　network　compression　is　a　widely　used　technique　when　deploying　the　neural　network　in　energy-constrained　and　computation-constrained　devices.　To　guarantee　the　compressed　neural　network　is　still　usable　without　too　much　accuracy　loss,　the　equivalence　between　the　compressed　networks　and　the　original　ones　must　be　verified.　However,　the　current　verification　approach　can　only　verify　the　equivalence　of　the　two　neural　networks　in　the　same　structure　which　is　limited　and　unseen　in　real-world　scenarios.　In　this　paper,　we　proposed　an　equivalence　verification　method　named　VeriPrune,　which　can　verify　the　equivalence　of　the　deep　neural　networks　without　the　structure　limitation.　In　detail,　we　proposed　an　innovative　virtual　node　completing　method　to　solve　the　problem　that　node　pruning　causes　structure　change　and　invalidates　the　existing　equivalence　verification　approaches.　To　demonstrate　the　feasibility　and　efficiency　of　the　proposed　approach,　we　conducted　experiments　on　the　public　dataset　with　49　DNNs　and　1272　properties.　The　results　show　that　the　83.9%　properties,　the　1067　of　the　total　1272　properties　can　be　verified.　The　proposed　VeriPrune　can　be　further　developed　as　a　CASE　tool　for　the　industry　settings.　©　2024