Graph-based　semi-supervised　learning　(GSSL)　attracts　considerable　attention　in　recent　years.　The　performance　of　a　general　GSSL　method　relies　on　the　quality　of　Laplacian　weighted　graph　(LWR)　composed　of　the　similarity　imposed　on　input　examples.　A　key　for　constructing　an　effective　LWR　is　on　the　proper　selection　of　the　neighborhood　size　K　or　epsilon　on　the　construction　of　KNN　graph　or　epsilon-neighbor　graph　on　training　samples,　which　constitutes　the　fundamental　elements　in　LWR.　Specifically,　too　large　K　or　epsilon　will　result　in　＂shortcut＂　phenomenon　while　too　small　ones　cannot　guarantee　to　represent　a　complete　manifold　structure　underlying　data.　To　this　issue,　this　study　attempts　to　propose　a　method,　called　adaptive　Laplacian　graph　trimming　(ALGT),　to　make　an　automatic　tuning　to　cut　improper　inter-cluster　shortcut　edges　while　enhance　the　connection　between　intra-cluster　samples,　so　as　to　adaptively　fit　a　proper　LWR　from　data.　The　superiority　of　the　proposed　method　is　substantiated　by　experimental　results　implemented　on　synthetic　and　UCI　data　sets.　(C)　2016　Elsevier　B.V.　All　rights　reserved.