[
  {
    "Accuracy": 57.9,
    "code_links": [
      {
        "title": "tkipf/gcn",
        "url": "https://github.com/tkipf/gcn"
      },
      {
        "title": "tkipf/pygcn",
        "url": "https://github.com/tkipf/pygcn"
      },
      {
        "title": "stellargraph/stellargraph",
        "url": "https://github.com/stellargraph/stellargraph"
      },
      {
        "title": "tkipf/gae",
        "url": "https://github.com/tkipf/gae"
      },
      {
        "title": "google/flax",
        "url": "https://github.com/google/flax/tree/master/examples/graph"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "title": "zhao-tong/GNNs-easy-to-use",
        "url": "https://github.com/zhao-tong/GNNs-easy-to-use"
      },
      {
        "title": "Tioz90/GCN",
        "url": "https://github.com/Tioz90/GCN"
      },
      {
        "title": "Tioz90/DiffPool",
        "url": "https://github.com/Tioz90/DiffPool"
      },
      {
        "title": "gcucurull/jax-gcn",
        "url": "https://github.com/gcucurull/jax-gcn"
      },
      {
        "title": "liambll/drug-efficacy-prediction",
        "url": "https://github.com/liambll/drug-efficacy-prediction"
      },
      {
        "title": "liambll/drug-activity-prediction",
        "url": "https://github.com/liambll/drug-activity-prediction"
      },
      {
        "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"
      },
      {
        "title": "BIG-S2/keras-gnm",
        "url": "https://github.com/BIG-S2/keras-gnm"
      },
      {
        "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"
      },
      {
        "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"
      },
      {
        "title": "KimMeen/GCN",
        "url": "https://github.com/KimMeen/GCN"
      },
      {
        "title": "HoganZhang/pygcn_python3",
        "url": "https://github.com/HoganZhang/pygcn_python3"
      },
      {
        "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"
      },
      {
        "title": "ovchinnikovdk/graph_clf",
        "url": "https://github.com/ovchinnikovdk/graph_clf"
      },
      {
        "title": "mlzxzhou/keras-gnm",
        "url": "https://github.com/mlzxzhou/keras-gnm"
      },
      {
        "title": "bmy4415/DMLAB-intern",
        "url": "https://github.com/bmy4415/DMLAB-intern"
      },
      {
        "title": "zxhhh97/ABot",
        "url": "https://github.com/zxhhh97/ABot"
      },
      {
        "title": "darnbi/pygcn",
        "url": "https://github.com/darnbi/pygcn"
      }
    ],
    "date": "2016-09-09",
    "date2": 20160909,
    "model": "GCN",
    "paper": {
      "title": "Semi-Supervised Classification with Graph Convolutional Networks",
      "url": "https://cknow.io/lib/97bb7dd114c0d906"
    },
    "paper_data_uoa": "97bb7dd114c0d906"
  },
  {
    "Accuracy": 50.9,
    "code_links": [
      {
        "title": "aymericdamien/TopDeepLearning",
        "url": "https://github.com/aymericdamien/TopDeepLearning"
      },
      {
        "title": "PetarV-/GAT",
        "url": "https://github.com/PetarV-/GAT"
      },
      {
        "title": "Diego999/pyGAT",
        "url": "https://github.com/Diego999/pyGAT"
      },
      {
        "title": "shenweichen/GraphNeuralNetwork",
        "url": "https://github.com/shenweichen/GraphNeuralNetwork"
      },
      {
        "title": "danielegrattarola/keras-gat",
        "url": "https://github.com/danielegrattarola/keras-gat"
      },
      {
        "title": "HazyResearch/hgcn",
        "url": "https://github.com/HazyResearch/hgcn"
      },
      {
        "title": "GraphSAINT/GraphSAINT",
        "url": "https://github.com/GraphSAINT/GraphSAINT"
      },
      {
        "title": "zhao-tong/GNNs-easy-to-use",
        "url": "https://github.com/zhao-tong/GNNs-easy-to-use"
      },
      {
        "title": "weiyangfb/PyTorchSparseGAT",
        "url": "https://github.com/weiyangfb/PyTorchSparseGAT"
      },
      {
        "title": "snowkylin/gnn",
        "url": "https://github.com/snowkylin/gnn"
      },
      {
        "title": "HeapHop30/graph-attention-nets",
        "url": "https://github.com/HeapHop30/graph-attention-nets"
      },
      {
        "title": "ds4dm/sparse-gcn",
        "url": "https://github.com/ds4dm/sparse-gcn"
      },
      {
        "title": "ds4dm/sGat",
        "url": "https://github.com/ds4dm/sGat"
      },
      {
        "title": "Kaimaoge/IGNNK",
        "url": "https://github.com/Kaimaoge/IGNNK"
      },
      {
        "title": "gcucurull/jax-gat",
        "url": "https://github.com/gcucurull/jax-gat"
      },
      {
        "title": "marble0117/GNN_models_pytorch_geometric",
        "url": "https://github.com/marble0117/GNN_models_pytorch_geometric"
      },
      {
        "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": "Yindong-Zhang/myGAT",
        "url": "https://github.com/Yindong-Zhang/myGAT"
      },
      {
        "title": "marblet/gat-pytorch",
        "url": "https://github.com/marblet/gat-pytorch"
      },
      {
        "title": "calciver/Graph-Attention-Networks",
        "url": "https://github.com/calciver/Graph-Attention-Networks"
      },
      {
        "title": "BIG-S2/keras-gnm",
        "url": "https://github.com/BIG-S2/keras-gnm"
      },
      {
        "title": "fongyk/graph-attention",
        "url": "https://github.com/fongyk/graph-attention"
      },
      {
        "title": "noahtren/Graph-Attention-Networks-TensorFlow-2",
        "url": "https://github.com/noahtren/Graph-Attention-Networks-TensorFlow-2"
      },
      {
        "title": "handasontam/GAT-with-edgewise-attention",
        "url": "https://github.com/handasontam/GAT-with-edgewise-attention"
      },
      {
        "title": "zxhhh97/ABot",
        "url": "https://github.com/zxhhh97/ABot"
      },
      {
        "title": "mitya8128/experiments_notes",
        "url": "https://github.com/mitya8128/experiments_notes"
      },
      {
        "title": "subercui/pyGConvAT",
        "url": "https://github.com/subercui/pyGConvAT"
      },
      {
        "title": "Anak2016/GAT",
        "url": "https://github.com/Anak2016/GAT"
      },
      {
        "title": "ChengSashankh/trying-GAT",
        "url": "https://github.com/ChengSashankh/trying-GAT"
      },
      {
        "title": "mlzxzhou/keras-gnm",
        "url": "https://github.com/mlzxzhou/keras-gnm"
      },
      {
        "title": "YunseobShin/wiki_GAT",
        "url": "https://github.com/YunseobShin/wiki_GAT"
      },
      {
        "title": "ColdenChan/GAT_D",
        "url": "https://github.com/ColdenChan/GAT_D"
      },
      {
        "title": "snownus/COOP",
        "url": "https://github.com/snownus/COOP"
      },
      {
        "title": "ChengyuSun/gat",
        "url": "https://github.com/ChengyuSun/gat"
      }
    ],
    "date": "2017-10-30",
    "date2": 20171030,
    "model": "GAT",
    "paper": {
      "title": "Graph Attention Networks",
      "url": "https://cknow.io/lib/42fabb81d929f34c"
    },
    "paper_data_uoa": "42fabb81d929f34c"
  },
  {
    "Accuracy": 45.4,
    "code_links": [
      {
        "title": "williamleif/GraphSAGE",
        "url": "https://github.com/williamleif/GraphSAGE"
      },
      {
        "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"
    },
    "paper_data_uoa": "89937fcf133ed302"
  },
  {
    "Accuracy": 45.3,
    "code_links": [
      {
        "title": "tkipf/gcn",
        "url": "https://github.com/tkipf/gcn"
      },
      {
        "title": "mdeff/cnn_graph",
        "url": "https://github.com/mdeff/cnn_graph"
      },
      {
        "title": "xbresson/spectral_graph_convnets",
        "url": "https://github.com/xbresson/spectral_graph_convnets"
      },
      {
        "title": "sk1712/gcn_metric_learning",
        "url": "https://github.com/sk1712/gcn_metric_learning"
      },
      {
        "title": "SwissDataScienceCenter/DeepSphere",
        "url": "https://github.com/SwissDataScienceCenter/DeepSphere"
      },
      {
        "title": "deepsphere/DeepSphere",
        "url": "https://github.com/deepsphere/DeepSphere"
      },
      {
        "title": "sheryl-ai/MVGCN",
        "url": "https://github.com/sheryl-ai/MVGCN"
      },
      {
        "title": "sheryl-ai/MemGCN",
        "url": "https://github.com/sheryl-ai/MemGCN"
      },
      {
        "title": "BinqingWu/2019-ZJU_SummerResearch",
        "url": "https://github.com/BinqingWu/2019-ZJU_SummerResearch"
      },
      {
        "title": "Fuhongshuai/FUHS_GraphCNN",
        "url": "https://github.com/Fuhongshuai/FUHS_GraphCNN"
      },
      {
        "title": "brain-bzh/MCNN",
        "url": "https://github.com/brain-bzh/MCNN"
      },
      {
        "title": "rishabhguptagithub/graph_convnet_retina",
        "url": "https://github.com/rishabhguptagithub/graph_convnet_retina"
      },
      {
        "title": "mdeff/paper-cnn-graph-nips2016",
        "url": "https://github.com/mdeff/paper-cnn-graph-nips2016"
      },
      {
        "title": "DebagMASTA/test2",
        "url": "https://github.com/DebagMASTA/test2"
      },
      {
        "title": "zdcuob/cnn_graph-",
        "url": "https://github.com/zdcuob/cnn_graph-"
      }
    ],
    "date": "2016-06-30",
    "date2": 20160630,
    "model": "ChebyNet",
    "paper": {
      "title": "Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering",
      "url": "https://cknow.io/lib/ec059442cb6a9940"
    },
    "paper_data_uoa": "ec059442cb6a9940"
  }
]