{"id":13688571,"url":"https://github.com/woojeongjin/dynamic-KG","last_synced_at":"2025-05-01T19:31:07.040Z","repository":{"id":37390677,"uuid":"151775236","full_name":"woojeongjin/dynamic-KG","owner":"woojeongjin","description":"Dynamic (Temporal) Knowledge Graph Completion (Reasoning)","archived":false,"fork":false,"pushed_at":"2020-09-15T19:55:03.000Z","size":25,"stargazers_count":588,"open_issues_count":0,"forks_count":112,"subscribers_count":31,"default_branch":"master","last_synced_at":"2024-11-12T12:47:43.975Z","etag":null,"topics":["dynamic","knowledge-base","knowledge-graph","knowledge-graph-completion","temporal-data"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/woojeongjin.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2018-10-05T20:35:05.000Z","updated_at":"2024-11-04T09:37:28.000Z","dependencies_parsed_at":"2022-07-07T23:09:30.020Z","dependency_job_id":null,"html_url":"https://github.com/woojeongjin/dynamic-KG","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/woojeongjin%2Fdynamic-KG","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/woojeongjin%2Fdynamic-KG/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/woojeongjin%2Fdynamic-KG/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/woojeongjin%2Fdynamic-KG/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/woojeongjin","download_url":"https://codeload.github.com/woojeongjin/dynamic-KG/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251932654,"owners_count":21667189,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["dynamic","knowledge-base","knowledge-graph","knowledge-graph-completion","temporal-data"],"created_at":"2024-08-02T15:01:16.804Z","updated_at":"2025-05-01T19:31:06.783Z","avatar_url":"https://github.com/woojeongjin.png","language":null,"funding_links":[],"categories":["Others","图嵌入、网络表征学习","Categories"],"sub_categories":["网络服务_其他","Embedding, Representation Learning, and Deep Learning on Graphs"],"readme":"# Dynamic Knowledge Graph Completion\nThis page is to summarize important materials about *dynamic (temporal) knowledge graph completion* and *dynamic graph embedding*.\n\n# Bookmarks\n- [Temporal Knowledge Graph Completion](#Temporal-knowledge-graph-completion-/-reasoning)\n- [Dynamic Graph Embedding](#Dynamic-graph-embedding)\n- [Knowledge Graph Embedding](#Knowledge-graph-embedding)\n- [Static Graph Embedding](#Static-graph-embedding)\n- [Survey](#Survey)\n- [Others](#Others)\n- [Useful Libararies](#Useful-Libararies)\n\n## Temporal Knowledge Graph Completion / Reasoning\n- [Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs](https://arxiv.org/abs/1904.05530)\n\t- Woojeong Jin, Meng Qu, Xisen Jin, Xiang Ren. EMNLP 2020.\n\t\t- This work is on an *extrapolation* problem which is to make predictions at unobserved times, different from interpolation work.\n\t\t- Proposes a novel neural architecture for modeling complex entity interaction sequences, which consists of a *recurrent event encoder* and a *neighborhood aggregator*.\n\t\t- Explores various neighborhood aggregators: a multi-relational graph aggregator demonstrates its effectiveness among them.\n\t\t- [Code and Data](https://github.com/INK-USC/re-net)\n\u003c!-- - [Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs](https://arxiv.org/abs/1705.05742)\n\t- Rakshit Trivedi, Hanjun Dai, Yichen Wang, Le Song. ICML 2017.\n\t- [Video](https://vimeo.com/238228194)\n\t- [Code (cpp)](https://github.com/rstriv/Know-Evolve) --\u003e\n- [Learning Sequence Encoders for Temporal Knowledge Graph Completion](https://arxiv.org/abs/1809.03202) (Interpolation)\n\t- Alberto Garcia-Duran, Sebastijan Dumancic, Mathias Niepert. EMNLP 2018.\n- [Towards time-aware knowledge graph completion](http://aclweb.org/anthology/C16-1161) (Interpolation)\n\t- Tingsong Jiang, Tianyu Liu, Tao Ge, Lei Sha, Baobao Chang, Sujian Li and Zhifang Sui. COLING 2016.\n- [Deriving validity time in knowledge graph](https://dl.acm.org/citation.cfm?id=3191639) (Interpolation)\n\t- Julien Leblay and Melisachew Wudage Chekol. WWW Workshop 2018.\n- [HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding](http://aclweb.org/anthology/D18-1225) (Interpolation)\n\t- Shib Sankar Dasgupta, Swayambhu Nath Ray, Partha Talukdar. EMNLP 2018.\n\t- [Code (TF based)](https://github.com/malllabiisc/HyTE)\n- [Predicting the co-evolution of event and knowledge graphs](https://arxiv.org/abs/1512.06900)\n\t- Cristóbal Esteban, Volker Tresp, Yinchong Yang, Stephan Baier, Denis Krompaß. FUSION 2016.\n- [Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition](https://arxiv.org/pdf/1911.07893.pdf)\n- [Diachronic Embedding for Temporal Knowledge Graph Completion](https://grlearning.github.io/papers/41.pdf)\n- [Hybrid-TE: Hybrid Translation-based Temporal Knowledge Graph Embedding](http://www-kb.is.s.u-tokyo.ac.jp/~li-xin/ictai19.pdf)\n- [Tensor Decompositions for Temporal Knowledge Base Completion](https://openreview.net/forum?id=rke2P1BFwS)\n\n## Dynamic Graph Embedding\n- [DyREP: Learning Representations over Dynamic Graphs](https://openreview.net/forum?id=HyePrhR5KX) (Extrapolation)\n\t- Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, Hongyuan Zha. ICLR 2019.\n- [DynGEM: Deep Embedding Method for Dynamic Graphs](https://arxiv.org/abs/1805.11273)\n\t- Palash Goyal, Nitin Kamra, Xinran He, Yan Liu. IJCAI 2017.\n- [Graph2Seq: Scalable Learning Dynamics for Graphs](https://openreview.net/forum?id=Ske7ToC5Km)\n\t- Shaileshh Bojja Venkatakrishnan, Mohammad Alizadeh, Pramod Viswanath\n- [Dynamic Graph Representation Learning via Self-Attention Networks](https://openreview.net/forum?id=HylsgnCcFQ)\n\t- Aravind Sankar, Yanhong Wu, Liang Gou, Wei Zhang, Hao Yang\n- [Continuous-Time Dynamic Network Embeddings](http://ryanrossi.com/pubs/nguyen-et-al-WWW18-BigNet.pdf)\n\t- Giang Hoang Nguyen, John Boaz Lee, Ryan A. Rossi, Nesreen K. Ahmed, Eunyee Koh, Sungchul Kim. WWW 2018.\n- [GC-LSTM: Graph Convolution Embedded LSTM for Dynamic Link Prediction](https://arxiv.org/pdf/1812.04206.pdf)\n\t- Jinyin Chen, Xuanheng Xu, Yangyang Wu, Haibin Zheng\n- [Learning Dynamic Embeddings from Temporal Interaction Networks](https://www-cs.stanford.edu/~srijan/pubs/paper-interactions.pdf)\n\t- Srijan Kumar, Xikun Zhang, Jure Leskovec\n- [Dynamic Graph Convolutional Networks](https://arxiv.org/pdf/1704.06199.pdf)\n\t- Franco Manessi, Alessandro Rozza, Mario Manzo\n- [Streaming Graph Neural Networks](https://arxiv.org/pdf/1810.10627.pdf)\n\t- Yao Ma, Ziyi Guo, Zhaochun Ren, Eric Zhao, Jiliang Tang, Dawei Yin\n\u003c!-- - [dynnode2vec: Scalable Dynamic Network Embedding](https://arxiv.org/abs/1812.02356)\n \t- Sedigheh Mahdavi, Shima Khoshraftar, Aijun An --\u003e\n- [Dynamic Network Embedding: An Extended Approach for Skip-gram based Network Embedding](https://www.ijcai.org/proceedings/2018/0288.pdf)\n\t- Lun Du, Yun Wang, Guojie Song, Zhicong Lu, Junshan Wang\n- [EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs](https://arxiv.org/abs/1902.10191)\n\t- Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Charles E. Leisersen, ArXiv.\n- [Gated Residual Recurrent Graph Neural Networks for Traffic Prediction](https://oar.a-star.edu.sg/jspui/bitstream/123456789/3020/1/AAAI-ChenC.4591.pdf#page8)\n\t- Cen Chen, Kenli Li, Sin G. Teo, Xiaofeng Zou, Kang Wang, Jie Wang, Zeng Zeng, AAAI 2019.\n- [Structured Sequence Modeling with Graph Convolutional Recurrent Networks](https://arxiv.org/abs/1612.07659)\n\t- Youngjoo Seo, Michaël Defferrard, Pierre Vandergheynst, Xavier Bresson, ICONIP 2017.\n- [Dynamic Network Embedding by Modeling Triadic Closure Process](http://yangy.org/works/dynamictriad/dynamic_triad.pdf)\n\t- Lekui Zhou, Yang Yang, Xiang Ren, Fei Wu, Yueting Zhuang. AAAI 2018.\n- [DynGAN: Generative Adversarial Networks for Dynamic Network Embedding](https://grlearning.github.io/papers/66.pdf)\n\t- Ayush Maheshwari, Ayush Goyal, Manjesh Kumar Hanawal, Ganesh Ramakrishnan. NeurIPS 2019 Workshop.\n\n\n## Knowledge Graph Embedding\n- [Modeling Relational Data with Graph Convolutional Networks](https://arxiv.org/abs/1703.06103)\n\t- Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling. ESWC 2018.\n\t- [Code (Keras based)](https://github.com/tkipf/relational-gcn), [Code (TF based)](https://github.com/MichSchli/RelationPrediction)\n- [Neural Relational Inference for Interacting Systems](https://arxiv.org/abs/1802.04687)\n\t- Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard Zemel. ICML 2018.\n\t- [Code (Pytorch based)](https://github.com/ethanfetaya/NRI)\n- [Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs](https://arxiv.org/abs/1812.00279)\n\t- Daniel Neil, Joss Briody, Alix Lacoste, Aaron Sim, Paidi Creed, Amir Saffari. ICONIP 2017.\n\n## Static Graph Embedding\n- [Inductive Representation Learning on Large Graphs](https://www-cs-faculty.stanford.edu/people/jure/pubs/graphsage-nips17.pdf)\n\t- William L. Hamilton, Rex Ying, Jure Leskovec\n\t- [Code (TF based)](https://github.com/williamleif/GraphSAGE), [Code (Pytorch based)](https://github.com/williamleif/graphsage-simple/)\n- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://arxiv.org/pdf/1806.01973)\n\t- Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, Jure Leskovec\n- [Stochastic Training of Graph Convolutional Networks with Variance Reduction](https://arxiv.org/pdf/1710.10568.pdf)\n\t- Jianfei Chen, Jun Zhu, Le Song\n- [A Higher-Order Graph Convolutional Layer](http://sami.haija.org/papers/high-order-gc-layer.pdf)\n\t- Sami Abu-El-Haija, Nazanin Alipourfard, Hrayr Harutyunyan, Amol Kapoor, Bryan Perozzi\n- [Higher-order Graph Convolutional Networks](http://ryanrossi.com/pubs/Higher-order-GCNs.pdf)\n\t- John Boaz Lee, Ryan A. Rossi, Xiangnan Kong, Sungchul Kim, Eunyee Koh, and Anup Rao\n\n\n## Other Survey Papers\n- [Deep Learning on Graphs: A Survey](https://arxiv.org/abs/1812.04202)\n\t- Ziwei Zhang, Peng Cui, Wenwu Zhu\n- [Graph Neural Networks: A Review of Methods and Applications](https://arxiv.org/abs/1812.08434)\n\t- Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Maosong Sun\n- [A Comprehensive Survey on Graph Neural Networks](https://arxiv.org/abs/1901.00596)\n\t- Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu\n- [A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications](https://arxiv.org/abs/1709.07604)\n\t- Hongyun Cai, Vincent W. Zheng, Kevin Chen-Chuan Chang\n- [How Powerful are Graph Neural Networks?](https://arxiv.org/abs/1810.00826)\n\t- Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka. ICLR 2019.\n- [Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey](https://arxiv.org/abs/1905.11485)\n\t- Seyed Mehran Kazemi, Rishab Goel, Kshitij Jain, Ivan Kobyzev, Akshay Sethi, Peter Forsyth, Pascal Poupart\n\n## Others\n- [Temporal Convolutional Networks: A Unified Approach to Action Segmentation](https://arxiv.org/abs/1608.08242)\n\t- Colin Lea, Rene Vidal, Austin Reiter, Gregory D. Hager\n- [What to Do Next: Modeling User Behaviors by Time-LSTM](https://www.ijcai.org/proceedings/2017/0504.pdf)\n\t- Yu Zhu, Hao Li, Yikang Liao, Beidou Wang, Ziyu Guan, Haifeng Liu, Deng Cai. IJCAI 2017.\n- [Patient Subtyping via Time-Aware LSTM Networks](http://biometrics.cse.msu.edu/Publications/MachineLearning/Baytasetal_PatientSubtypingViaTimeAwareLSTMNetworks.pdf)\n\t- Inci M. Baytas, Cao Xiao, Xi Zhang, Fei Wang, Anil K. Jain, Jiayu Zhou. KDD 2017.\n\n## Useful Libararies\n- [Deep graph library](https://www.dgl.ai)\n- [Pytorch geometric](https://github.com/rusty1s/pytorch_geometric)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwoojeongjin%2Fdynamic-KG","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwoojeongjin%2Fdynamic-KG","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwoojeongjin%2Fdynamic-KG/lists"}