Graph　Convolutional　Network　(GCN)　is　a　powerful　model　for　graph　representation　learning.　Since　GCN　updates　nodes　with　a　recursive　neighbor　aggregation　scheme,　training　GCN　on　large-scale　graphs　suffers　from　enormous　computational　cost　and　large　memory　requirement.　The　subgraph　sampling　method　trains　GCN　on　sampled　small-scale　subgraphs　to　speed　up　GCN.　However,　they　also　suffer　from　problems,　such　as　training　GCN　on　unconnected　and　scale-unbalanced　subgraphs,　which　reduce　performance　and　efficiency.　Moreover,　existing　subgraph　sampling　methods　train　GCN　on　subgraphs　independently　and　ignore　the　relation　information　among　different　subgraphs.　This　paper　proposes　a　novel　subgraph　sampling　method,　Improved　Adaptive　Neighbor　Sampling　(IANS),　and　a　novel　loss　function,　Subgraph　Contrastive　Loss.　Subgraphs　sampled　by　the　IANS　method　are　scale-balanced,　inside　nodes　are　significantly　relevant,　and　the　sample　ratio　controls　the　sparsity　of　subgraphs.　To　recover　the　lost　relation　information　between　different　subgraphs,　the　Subgraph　Contrastive　Loss　is　defined,　which　constrains　the　initially　connected　nodes　in　different　subgraphs　to　be　closer　and　pushes　unconnected　nodes　far　away　in　feature　space.　A　series　of　experiments　are　conducted,　which　train　GCN　with　IANS　and　Subgraph　Contrastive　Loss　for　node　classification　on　three　different　scale　datasets.　The　training　time　and　classification　accuracy　demonstrate　the　effectiveness　of　the　proposed　method.　©　2023　Elsevier　Inc.