动态知识图谱补全论文合集

【导读】知识图谱补全算法能让知识图谱变得更加完整,目前是人工智能领域的一个研究热点。为了更好地给出补全算法综述,文章按照能否处理新实体或者新关系,将知识图谱补全算法分成两类:静态知识图谱补全算法以及动态知识图谱补全算法。前者仅能处理实体以及关系都是固定的场景,扩展性较差。后者可以处理含有新实体或者新关系的场景,能够构造动态的知识图谱,具有更好的现实意义。

woojeongjin在Github上整理了关于动态知识图谱补全的文章

https://github.com/woojeongjin/dynamic-KG，欢迎查看

• Temporal Knowledge Graph Completion

• Dynamic Graph Embedding

• Knowledge Graph Embedding

• Static Graph Embedding

• Survey

• Others

• Useful Libararies

Temporal Knowledge Graph Completion

• Learning Sequence Encoders for Temporal Knowledge Graph Completion

• Alberto Garcia-Duran, Sebastijan Dumancic, Mathias Niepert. EMNLP 2018.

• Towards time-aware knowledge graph completion

• Tingsong Jiang, Tianyu Liu, Tao Ge, Lei Sha, Baobao Chang, Sujian Li and Zhifang Sui. COLING 2016.

• Predicting the co-evolution of event and knowledge graphs

• Cristóbal Esteban, Volker Tresp, Yinchong Yang, Stephan Baier, Denis Krompaß. FUSION 2016.

• Deriving validity time in knowledge graph

• Julien Leblay and Melisachew Wudage Chekol. WWW Workshop 2018.

• HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding

• Shib Sankar Dasgupta, Swayambhu Nath Ray, Partha Talukdar. EMNLP 2018.

• Code (TF based)

Dynamic Graph Embedding

• DyREP: Learning Representations over Dynamic Graphs

• Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, Hongyuan Zha. ICLR 2019.

• DynGEM: Deep Embedding Method for Dynamic Graphs

• Palash Goyal, Nitin Kamra, Xinran He, Yan Liu. IJCAI 2017.

• Graph2Seq: Scalable Learning Dynamics for Graphs

• Shaileshh Bojja Venkatakrishnan, Mohammad Alizadeh, Pramod Viswanath

• Dynamic Graph Representation Learning via Self-Attention Networks

• Aravind Sankar, Yanhong Wu, Liang Gou, Wei Zhang, Hao Yang

• Continuous-Time Dynamic Network Embeddings

• Giang Hoang Nguyen, John Boaz Lee, Ryan A. Rossi, Nesreen K. Ahmed, Eunyee Koh, Sungchul Kim. WWW 2018.

• GC-LSTM: Graph Convolution Embedded LSTM for Dynamic Link Prediction

• Jinyin Chen, Xuanheng Xu, Yangyang Wu, Haibin Zheng

• Learning Dynamic Embeddings from Temporal Interaction Networks

• Srijan Kumar, Xikun Zhang, Jure Leskovec

• Dynamic Graph Convolutional Networks

• Franco Manessi, Alessandro Rozza, Mario Manzo

• Streaming Graph Neural Networks

• Yao Ma, Ziyi Guo, Zhaochun Ren, Eric Zhao, Jiliang Tang, Dawei Yin

• Dynamic Network Embedding: An Extended Approach for Skip-gram based Network Embedding

• Lun Du, Yun Wang, Guojie Song, Zhicong Lu, Junshan Wang

• EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs

• Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Charles E. Leisersen, ArXiv.

• Gated Residual Recurrent Graph Neural Networks for Traffic Prediction

• Cen Chen, Kenli Li, Sin G. Teo, Xiaofeng Zou, Kang Wang, Jie Wang, Zeng Zeng, AAAI 2019.

• Structured Sequence Modeling with Graph Convolutional Recurrent Networks

• Youngjoo Seo, Michaël Defferrard, Pierre Vandergheynst, Xavier Bresson, ICONIP 2017.

• Dynamic Network Embedding by Modeling Triadic Closure Process

• Lekui Zhou, Yang Yang, Xiang Ren, Fei Wu, Yueting Zhuang. AAAI 2018.

Knowledge Graph Embedding

• Modeling Relational Data with Graph Convolutional Networks

• Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling. ESWC 2018.

• Code (Keras based), Code (TF based)

• Neural Relational Inference for Interacting Systems

• Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard Zemel. ICML 2018.

• Code (Pytorch based)

• Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs

• Daniel Neil, Joss Briody, Alix Lacoste, Aaron Sim, Paidi Creed, Amir Saffari. ICONIP 2017.

Static Graph Embedding

• Inductive Representation Learning on Large Graphs

• William L. Hamilton, Rex Ying, Jure Leskovec

• Code (TF based), Code (Pytorch based)

• Graph Convolutional Neural Networks for Web-Scale Recommender Systems

• Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, Jure Leskovec

• Stochastic Training of Graph Convolutional Networks with Variance Reduction

• Jianfei Chen, Jun Zhu, Le Song

• A Higher-Order Graph Convolutional Layer

• Sami Abu-El-Haija, Nazanin Alipourfard, Hrayr Harutyunyan, Amol Kapoor, Bryan Perozzi

• Higher-order Graph Convolutional Networks

• John Boaz Lee, Ryan A. Rossi, Xiangnan Kong, Sungchul Kim, Eunyee Koh, and Anup Rao

Survey

• Deep Learning on Graphs: A Survey

• Ziwei Zhang, Peng Cui, Wenwu Zhu

• Graph Neural Networks: A Review of Methods and Applications

• Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Maosong Sun

• A Comprehensive Survey on Graph Neural Networks

• Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu

• A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications

• Hongyun Cai, Vincent W. Zheng, Kevin Chen-Chuan Chang

• How Powerful are Graph Neural Networks?

• Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka. ICLR 2019.

Others

• Temporal Convolutional Networks: A Unified Approach to Action Segmentation

• Colin Lea, Rene Vidal, Austin Reiter, Gregory D. Hager

• What to Do Next: Modeling User Behaviors by Time-LSTM

• Yu Zhu, Hao Li, Yikang Liao, Beidou Wang, Ziyu Guan, Haifeng Liu, Deng Cai. IJCAI 2017.

• Patient Subtyping via Time-Aware LSTM Networks

• Inci M. Baytas, Cao Xiao, Xi Zhang, Fei Wang, Anil K. Jain, Jiayu Zhou. KDD 2017.

Useful Libararies

• Deep graph library

• Pytorch geometric

-END-

专 · 知

专知，专业可信的人工智能知识分发，让认知协作更快更好！欢迎登录www.zhuanzhi.ai，注册登录专知，获取更多AI知识资料！

欢迎微信扫一扫加入专知人工智能知识星球群，获取最新AI专业干货知识教程视频资料和与专家交流咨询！

请加专知小助手微信（扫一扫如下二维码添加），加入专知人工智能主题群，咨询技术商务合作~

专知《深度学习:算法到实战》课程全部完成！530+位同学在学习，现在报名，限时优惠！网易云课堂人工智能畅销榜首位！

点击“阅读原文”，了解报名专知《深度学习:算法到实战》课程