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动态知识图谱补全论文合集

专知出品

【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


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