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    "Accuracy": 97.02,
    "code_links": [
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        "title": "DropEdge/DropEdge",
        "url": "https://github.com/DropEdge/DropEdge"
      },
      {
        "title": "GraphSAINT/GraphSAINT",
        "url": "https://github.com/GraphSAINT/GraphSAINT"
      }
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      "url": "https://cknow.io/lib/341f3a976a64b36b"
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    "Accuracy": 95.68,
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        "title": "tkipf/gcn",
        "url": "https://github.com/tkipf/gcn"
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        "title": "tkipf/pygcn",
        "url": "https://github.com/tkipf/pygcn"
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      {
        "title": "stellargraph/stellargraph",
        "url": "https://github.com/stellargraph/stellargraph"
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      {
        "title": "tkipf/gae",
        "url": "https://github.com/tkipf/gae"
      },
      {
        "title": "google/flax",
        "url": "https://github.com/google/flax/tree/master/examples/graph"
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      {
        "title": "Microsoft/gated-graph-neural-network-samples",
        "url": "https://github.com/Microsoft/gated-graph-neural-network-samples"
      },
      {
        "title": "tensorflow/neural-structured-learning",
        "url": "https://github.com/tensorflow/neural-structured-learning"
      },
      {
        "title": "tkipf/keras-gcn",
        "url": "https://github.com/tkipf/keras-gcn"
      },
      {
        "title": "shenweichen/GraphNeuralNetwork",
        "url": "https://github.com/shenweichen/GraphNeuralNetwork"
      },
      {
        "title": "bknyaz/graph_nn",
        "url": "https://github.com/bknyaz/graph_nn"
      },
      {
        "title": "HazyResearch/hgcn",
        "url": "https://github.com/HazyResearch/hgcn"
      },
      {
        "title": "VoVAllen/diffpool",
        "url": "https://github.com/VoVAllen/diffpool"
      },
      {
        "title": "BrizziB/Graph-Classification-with-GCN",
        "url": "https://github.com/BrizziB/Graph-Classification-with-GCN"
      },
      {
        "title": "tkipf/ica",
        "url": "https://github.com/tkipf/ica"
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      {
        "title": "BinqingWu/2019-ZJU_SummerResearch",
        "url": "https://github.com/BinqingWu/2019-ZJU_SummerResearch"
      },
      {
        "title": "mahsa91/py_mgcn",
        "url": "https://github.com/mahsa91/py_mgcn"
      },
      {
        "title": "JHart96/keras_gcn_sequence_labelling",
        "url": "https://github.com/JHart96/keras_gcn_sequence_labelling"
      },
      {
        "title": "sufeidechabei/graphnets_gcn",
        "url": "https://github.com/sufeidechabei/graphnets_gcn"
      },
      {
        "title": "snowkylin/gnn",
        "url": "https://github.com/snowkylin/gnn"
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      {
        "title": "zhao-tong/GNNs-easy-to-use",
        "url": "https://github.com/zhao-tong/GNNs-easy-to-use"
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      {
        "title": "Tioz90/GCN",
        "url": "https://github.com/Tioz90/GCN"
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      {
        "title": "Tioz90/DiffPool",
        "url": "https://github.com/Tioz90/DiffPool"
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      {
        "title": "gcucurull/jax-gcn",
        "url": "https://github.com/gcucurull/jax-gcn"
      },
      {
        "title": "liambll/drug-efficacy-prediction",
        "url": "https://github.com/liambll/drug-efficacy-prediction"
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      {
        "title": "liambll/drug-activity-prediction",
        "url": "https://github.com/liambll/drug-activity-prediction"
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      {
        "title": "stellargraph/FisherGCN",
        "url": "https://github.com/stellargraph/FisherGCN"
      },
      {
        "title": "Kaimaoge/IGNNK",
        "url": "https://github.com/Kaimaoge/IGNNK"
      },
      {
        "title": "yangji9181/RELEARN",
        "url": "https://github.com/yangji9181/RELEARN"
      },
      {
        "title": "marblet/GNN_models_pytorch_geometric",
        "url": "https://github.com/marblet/GNN_models_pytorch_geometric"
      },
      {
        "title": "marble0117/GNN_models_pytorch",
        "url": "https://github.com/marble0117/GNN_models_pytorch"
      },
      {
        "title": "marble0117/GNN_models_pytorch_geometric",
        "url": "https://github.com/marble0117/GNN_models_pytorch_geometric"
      },
      {
        "title": "kaize0409/pygcn",
        "url": "https://github.com/kaize0409/pygcn"
      },
      {
        "title": "theodoriss/gcn-demo",
        "url": "https://github.com/theodoriss/gcn-demo"
      },
      {
        "title": "luohongyin/FG-GCN",
        "url": "https://github.com/luohongyin/FG-GCN"
      },
      {
        "title": "mcbaron/fluxGCN",
        "url": "https://github.com/mcbaron/fluxGCN"
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      {
        "title": "BIG-S2/keras-gnm",
        "url": "https://github.com/BIG-S2/keras-gnm"
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      {
        "title": "YijingZhang/GCN",
        "url": "https://github.com/YijingZhang/GCN"
      },
      {
        "title": "cvignac/gnn_statistics",
        "url": "https://github.com/cvignac/gnn_statistics"
      },
      {
        "title": "sbalnojan/graphcnn-examples",
        "url": "https://github.com/sbalnojan/graphcnn-examples"
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      {
        "title": "imayachita/Explore_GCN",
        "url": "https://github.com/imayachita/Explore_GCN"
      },
      {
        "title": "liuzhencheng/GCN_CODE",
        "url": "https://github.com/liuzhencheng/GCN_CODE"
      },
      {
        "title": "LeeWooJung/GCN_reproduce",
        "url": "https://github.com/LeeWooJung/GCN_reproduce"
      },
      {
        "title": "bcsrn/gcn",
        "url": "https://github.com/bcsrn/gcn"
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      {
        "title": "KimMeen/GCN",
        "url": "https://github.com/KimMeen/GCN"
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      {
        "title": "HoganZhang/pygcn_python3",
        "url": "https://github.com/HoganZhang/pygcn_python3"
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      {
        "title": "Aveek-Saha/Graph-Conv-Net",
        "url": "https://github.com/Aveek-Saha/Graph-Conv-Net"
      },
      {
        "title": "transcendentsky/dgl-project",
        "url": "https://github.com/transcendentsky/dgl-project"
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      {
        "title": "ovchinnikovdk/graph_clf",
        "url": "https://github.com/ovchinnikovdk/graph_clf"
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      {
        "title": "mlzxzhou/keras-gnm",
        "url": "https://github.com/mlzxzhou/keras-gnm"
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      {
        "title": "bmy4415/DMLAB-intern",
        "url": "https://github.com/bmy4415/DMLAB-intern"
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      {
        "title": "zxhhh97/ABot",
        "url": "https://github.com/zxhhh97/ABot"
      },
      {
        "title": "darnbi/pygcn",
        "url": "https://github.com/darnbi/pygcn"
      }
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    "date2": 20160909,
    "model": "GCN",
    "paper": {
      "title": "Semi-Supervised Classification with Graph Convolutional Networks",
      "url": "https://cknow.io/lib/97bb7dd114c0d906"
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    "Accuracy": 94.32,
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        "title": "williamleif/GraphSAGE",
        "url": "https://github.com/williamleif/GraphSAGE"
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      {
        "title": "stellargraph/stellargraph",
        "url": "https://github.com/stellargraph/stellargraph"
      },
      {
        "title": "pmlg/pytorch_GCN",
        "url": "https://github.com/pmlg/pytorch_GCN"
      },
      {
        "title": "zxhhh97/ABot",
        "url": "https://github.com/zxhhh97/ABot"
      },
      {
        "title": "Arvidsmile/InductiveDialogueGraph",
        "url": "https://github.com/Arvidsmile/InductiveDialogueGraph"
      }
    ],
    "date": "2017-06-07",
    "date2": 20170607,
    "model": "GraphSAGE",
    "paper": {
      "title": "Inductive Representation Learning on Large Graphs",
      "url": "https://cknow.io/lib/89937fcf133ed302"
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    "code_links": [
      {
        "title": "CRIPAC-DIG/GRACE",
        "url": "https://github.com/CRIPAC-DIG/GRACE"
      }
    ],
    "date": "2020-06-07",
    "date2": 20200607,
    "model": "GRACE",
    "paper": {
      "title": "Deep Graph Contrastive Representation Learning",
      "url": "https://cknow.io/lib/5f5ef1a976487392"
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    "paper_data_uoa": "5f5ef1a976487392"
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]