Purpose:　With　the　availability　and　utilization　of　Inter-Country　Input-Output　(ICIO)　tables,　it　is　possible　to　construct　quantitative　indices　to　assess　its　impact　on　the　Global　Value　Chain　(GVC).　For　the　sake　of　visualization,　ICIO　networks　with　tremendous　low-　weight　edges　are　too　dense　to　show　the　substantial　structure.　These　redundant　edges,　inevitably　make　the　network　data　full　of　noise　and　eventually　exert　negative　effects　on　Social　Network　Analysis　(SNA).　In　this　case,　we　need　a　method　to　filter　such　edges　and　obtain　a　sparser　network　with　only　the　meaningful　connections.　Design/methodology/approach:　In　this　paper,　we　propose　two　parameterless　pruning　algorithms　from　the　global　and　local　perspectives　respectively,　then　the　performance　of　them　is　examined　using　the　ICIO　table　from　different　databases.　Findings:　The　Searching　Paths　(SP)　method　extracts　the　strongest　association　paths　from　the　global　perspective,　while　Filtering　Edges　(FE)　method　captures　the　key　links　according　to　the　local　weight　ratio.　The　results　show　that　the　FE　method　can　basically　include　the　SP　method　and　become　the　best　solution　for　the　ICIO　networks.　Research　limitations:　There　are　still　two　limitations　in　this　research.　One　is　that　the　computational　complexity　may　increase　rapidly　while　processing　the　large-scale　networks,　so　the　proposed　method　should　be　further　improved.　The　other　is　that　much　more　empirical　networks　should　be　introduced　to　testify　the　scientificity　and　practicability　of　our　methodology.　Practical　implications:　The　network　pruning　methods　we　proposed　will　promote　the　analysis　of　the　ICIO　network,　in　terms　of　community　detection,　link　prediction,　and　spatial　econometrics,　etc.　Also,　they　can　be　applied　to　many　other　complex　networks　with　similar　characteristics.　Originality/value:　This　paper　improves　the　existing　research　from　two　aspects,　namely,　considering　the　heterogeneity　of　weights　and　avoiding　the　interference　of　parameters.　Therefore,　it　provides　a　new　idea　for　the　research　of　network　backbone　extraction.　　©　2022　Lizhi　Xing　et　al.,　published　by　Sciendo.