{"id":19565135,"url":"https://github.com/graph-0/rgtn-nie","last_synced_at":"2025-07-31T12:04:23.273Z","repository":{"id":59873512,"uuid":"374111865","full_name":"GRAPH-0/RGTN-NIE","owner":"GRAPH-0","description":"Implementation for the paper: Representation Learning on Knowledge Graphs for Node Importance 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RGTN-NIE\n\nDataset and code for Representation Learning on Knowledge Graphs for Node Importance Estimation.\n\n## NIE Dataset\n\n* FB15k: a subset from [FreeBase](https://developers.google.com/freebase).\n\n* TMDB5k: original files are from \n[Kaggle](https://www.kaggle.com/tmdb/tmdb-movie-metadata).\n\n* IMDB: original files are from [IMDb Datasets](\nhttps://www.imdb.com/interfaces/).\nWe provide the node text description files on [Google Drive](https://drive.google.com/file/d/10y6yIN6_y1Mw_83RKP32KISql_INjrWK/view?usp=sharing), and the graph construction files on [Google Drive](\nhttps://drive.google.com/file/d/1xd0ObAIDYMsxQZD2l0e-9fWo_ro76--x/view?usp=sharing).\n\n* Processed features: [Google Drive](https://drive.google.com/drive/folders/1mgcNhGHUTptTqRREJE-g-qKoGycVwKpV?usp=sharing).\nDownload the feature files and put them on 'datasets'.\n\n## Dependencies \n* pytorch 1.6.0\n* DGL 0.5.3\n\n## Training Examples\n\n* run `sh train_geni.sh` for GENI in FB15k (full batch training)\n* run `sh train_geni_batch.sh` for GENI in IMDB (minibatch training)\n* run `sh train_two.sh` for RGTN in FB15k (full batch training)\n* run `sh train_two_batch.sh` for RGTN in IMDB (minibatch training)\n\nNote that hyperparameters may require grid search in small datasets.\n\n\n## Citation\nIf you find our work useful for your reseach, please consider citing this paper:\n```bibtex\n@inproceedings{Huang21RGTN-NIE,\n  author    = {Han Huang and Leilei Sun and Bowen Du and Chuanren Liu and Weifeng Lv and Hui Xiong},\n  title     = {Representation Learning on Knowledge Graphs for Node Importance Estimation},\n  booktitle = {{KDD} '21: The 27th {ACM} {SIGKDD} Conference on Knowledge Discovery and Data Mining, Virtual Event, Singapore, August 14-18, 2021},\n  pages     = {646--655},\n  publisher = {{ACM}},\n  year      = {2021}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgraph-0%2Frgtn-nie","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgraph-0%2Frgtn-nie","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgraph-0%2Frgtn-nie/lists"}