{"task":"Node Classification","dataset":"AIFB","metric_names":["Accuracy"],"rows":[{"id":96268,"task":"Node Classification","parent_task":null,"dataset":"AIFB","model_name":"R-GCN","metrics":{"Accuracy":"95.83"},"paper_url":"http://arxiv.org/abs/1703.06103v4","paper_title":"Modeling Relational Data with Graph Convolutional Networks","paper_date":"2017-03-17","code_links":[{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/tensorflow/rgcn"},{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/rgcn-hetero"},{"title":"dmlc/dgl","url":"https://github.com/dmlc/dgl/tree/master/examples/mxnet/rgcn"},{"title":"tkipf/gae","url":"https://github.com/tkipf/gae"},{"title":"Microsoft/gated-graph-neural-network-samples","url":"https://github.com/Microsoft/gated-graph-neural-network-samples"},{"title":"tkipf/relational-gcn","url":"https://github.com/tkipf/relational-gcn"},{"title":"MichSchli/RelationPrediction","url":"https://github.com/MichSchli/RelationPrediction"},{"title":"INK-USC/RE-Net","url":"https://github.com/INK-USC/RE-Net"},{"title":"INK-USC/MHGRN","url":"https://github.com/INK-USC/MHGRN"},{"title":"shijx12/kqapro_baselines","url":"https://github.com/shijx12/kqapro_baselines"},{"title":"thiviyanT/torch-rgcn","url":"https://github.com/thiviyanT/torch-rgcn"},{"title":"berlincho/RGCN-pytorch","url":"https://github.com/berlincho/RGCN-pytorch"},{"title":"masakicktashiro/rgcn_pytorch_implementation","url":"https://github.com/masakicktashiro/rgcn_pytorch_implementation"},{"title":"dglai/wsdm2022-challenge","url":"https://github.com/dglai/wsdm2022-challenge"},{"title":"predict-idlab/RR-GCN","url":"https://github.com/predict-idlab/RR-GCN"},{"title":"giuseppefutia/semi","url":"https://github.com/giuseppefutia/semi"},{"title":"guillaumejaume/tuto-dl-on-graphs","url":"https://github.com/guillaumejaume/tuto-dl-on-graphs"},{"title":"anilakash/indkgc","url":"https://github.com/anilakash/indkgc"},{"title":"toooooodo/rgcn-linkprediction","url":"https://github.com/toooooodo/rgcn-linkprediction"},{"title":"JiabenLi/rgcn_paddlepaddle","url":"https://github.com/JiabenLi/rgcn_paddlepaddle"},{"title":"kracr/document-level-relation-extraction","url":"https://github.com/kracr/document-level-relation-extraction"},{"title":"susurrant/flow-imputation","url":"https://github.com/susurrant/flow-imputation"},{"title":"susurrant/spatial-interaction-modeling","url":"https://github.com/susurrant/spatial-interaction-modeling"},{"title":"parkererickson/crunchBaseGraph","url":"https://github.com/parkererickson/crunchBaseGraph"},{"title":"chalothon/Graph-Convolutional-Networks-for-Relational-Link-Prediction","url":"https://github.com/chalothon/Graph-Convolutional-Networks-for-Relational-Link-Prediction"},{"title":"QustKcz/relational-GCN","url":"https://github.com/QustKcz/relational-GCN"},{"title":"yangyucheng000/Papers","url":"https://github.com/yangyucheng000/Papers/tree/main/MindSpore-Retrieval-augmented"}],"metrics_order":"[\"Accuracy\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":96269,"task":"Node Classification","parent_task":null,"dataset":"AIFB","model_name":"RR-GCN-PPV-CUT","metrics":{"Accuracy":"95.83"},"paper_url":"https://arxiv.org/abs/2203.02424v2","paper_title":"R-GCN: The R Could Stand for Random","paper_date":"2022-03-04","code_links":[{"title":"predict-idlab/RR-GCN","url":"https://github.com/predict-idlab/RR-GCN"}],"metrics_order":"[\"Accuracy\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":96270,"task":"Node Classification","parent_task":null,"dataset":"AIFB","model_name":"SCENE","metrics":{"Accuracy":"95.83"},"paper_url":"https://arxiv.org/abs/2301.03512v1","paper_title":"SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks","paper_date":"2023-01-09","code_links":[{"title":"schmidt-ju/scene","url":"https://github.com/schmidt-ju/scene"}],"metrics_order":"[\"Accuracy\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":96271,"task":"Node Classification","parent_task":null,"dataset":"AIFB","model_name":"BoP","metrics":{"Accuracy":"92.22"},"paper_url":"https://arxiv.org/abs/2411.11149v1","paper_title":"From Primes to Paths: Enabling Fast Multi-Relational Graph Analysis","paper_date":"2024-11-17","code_links":[{"title":"kbogas/PAM_BoP","url":"https://github.com/kbogas/PAM_BoP"}],"metrics_order":"[\"Accuracy\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":96272,"task":"Node Classification","parent_task":null,"dataset":"AIFB","model_name":"Path Tree","metrics":{"Accuracy":"89.44"},"paper_url":"http://ceur-ws.org/Vol-2427/SEPDA_2019_paper_3.pdf","paper_title":"Inducing a Decision Tree with Discriminative Paths to Classify Entities in a Knowledge Graph","paper_date":"2019-08-22","code_links":[{"title":"IBCNServices/KGPTree","url":"https://github.com/IBCNServices/KGPTree"}],"metrics_order":"[\"Accuracy\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":96273,"task":"Node Classification","parent_task":null,"dataset":"AIFB","model_name":"RDF2Vec+SVM","metrics":{"Accuracy":"88.88"},"paper_url":"http://www.semantic-web-journal.net/content/rdf2vec-rdf-graph-embeddings-and-their-applications-1","paper_title":"RDF2Vec: RDF Graph Embeddings and Their Applications","paper_date":"2017-11-10","code_links":[{"title":"IBCNServices/pyRDF2Vec","url":"https://github.com/IBCNServices/pyRDF2Vec"}],"metrics_order":"[\"Accuracy\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]},{"id":96274,"task":"Node Classification","parent_task":null,"dataset":"AIFB","model_name":"RR-GCN-PPV","metrics":{"Accuracy":"86.11"},"paper_url":"https://arxiv.org/abs/2203.02424v2","paper_title":"R-GCN: The R Could Stand for Random","paper_date":"2022-03-04","code_links":[{"title":"predict-idlab/RR-GCN","url":"https://github.com/predict-idlab/RR-GCN"}],"metrics_order":"[\"Accuracy\"]","area":"Graphs","uses_additional_data":0,"source":"archive","tags":[]}]}