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Graph embedding methods such as Graph Neural Networks (GNNs) and Graph Transformers have contributed to the development of graph reasoning algorithms for various tasks on knowledge graphs.
Analogy and analogical reasoning
Paul Bartha · 2013
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Revisiting semi-supervised learning with graph embeddings
Zhilin Yang, William Cohen, and Ruslan Salakhudinov · 2016
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, Yoshua Bengio, et al · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling · 2018
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Rule-based and machine learning algorithms identify patients with systemic sclerosis accurately in the electronic health record
Lia Jamian, Lee Wheless, Leslie J Crofford, and April Barnado · 2019
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Clinical text classification with rule-based features and knowledge-guided convolutional neural networks
Liang Yao, Chengsheng Mao, and Yuan Luo · 2019
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A review on big data with machine learning and fuzzy logic for better decision making
J Betty Jane and EN Ganesh · 2019
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Simplifying graph convolutional networks
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger · 2019
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Graph neural networks: A review of methods and applications
Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun · 2020
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Symbolic logic meets machine learning: A brief survey in infinite domains
Vaishak Belle · 2020
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Heterogeneous graph transformer
Ziniu Hu, Yuxiao Dong, Kuansan Wang, and Yizhou Sun · 2020
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A generalization of transformer networks to graphs
Vijay Prakash Dwivedi and Xavier Bresson · 2020
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Knowledge graphs
Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d’Amato, Gerard De Melo, Claudio Gutierrez, Sabrina Kirrane, José Emilio Labra Gayo, Roberto Navigli, Sebastian Neumaier, et al · 2021
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Knowledge graph-empowered materials discovery
Xintong Zhao, Jane Greenberg, Scott McClellan, Yong-Jie Hu, Steven Lopez, Semion K Saikin, Xiaohua Hu, and Yuan An · 2021
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Hypergraph convolution and hypergraph attention
Song Bai, Feihu Zhang, and Philip HS Torr · 2021
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Deep learning–based text classification: a comprehensive review
Shervin Minaee, Nal Kalchbrenner, Erik Cambria, Narjes Nikzad, Meysam Chenaghlu, and Jianfeng Gao · 2021
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Training graph neural networks with 1000 layers
Guohao Li, Matthias Müller, Bernard Ghanem, and Vladlen Koltun · 2021
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Coarformer: Transformer for large graph via graph coarsening
Weirui Kuang, WANG Zhen, Yaliang Li, Zhewei Wei, and Bolin Ding · 2021
Cited alongside, same era.
Do transformers really perform badly for graph representation?
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu · 2021
Cited alongside, same era.
Lm-nav: Robotic navigation with large pre-trained models of language, vision, and action
Dhruv Shah, Błażej Osiński, Sergey Levine, et al · 2023
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Memorybank: Enhancing large language models with long-term memory
Wanjun Zhong, Lianghong Guo, Qiqi Gao, and Yanlin Wang · 2023
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Analogykb: Unlocking analogical reasoning of language models with a million-scale knowledge base
Siyu Yuan, Jiangjie Chen, Changzhi Sun, Jiaqing Liang, Yanghua Xiao, and Deqing Yang · 2023
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Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L Griffiths, Yuan Cao, and Karthik Narasimhan · 2023
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Enyan Dai, Tianxiang Zhao, Huaisheng Zhu, Junjie Xu, Zhimeng Guo, Hui Liu, Jiliang Tang, and Suhang Wang · 2022
Cited alongside, same era.
Toward better drug discovery with knowledge graph
Xiangxiang Zeng, Xinqi Tu, Yuansheng Liu, Xiangzheng Fu, and Yansen Su · 2022
Cited alongside, same era.
Minedojo: Building open-ended embodied agents with internet-scale knowledge
Linxi Fan, Guanzhi Wang, Yunfan Jiang, Ajay Mandlekar, Yuncong Yang, Haoyi Zhu, Andrew Tang, De-An Huang, Yuke Zhu, and Anima Anandkumar · 2022
Cited alongside, same era.
Transformer for graphs: An overview from architecture perspective
Erxue Min, Runfa Chen, Yatao Bian, Tingyang Xu, Kangfei Zhao, Wenbing Huang, Peilin Zhao, Junzhou Huang, Sophia Ananiadou, and Yu Rong · 2022
Cited alongside, same era.
Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou · 2022
Cited alongside, same era.
Revisiting heterophily for graph neural networks
Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Mingde Zhao, Shuyuan Zhang, Xiao-Wen Chang, and Doina Precup · 2022
Cited alongside, same era.
Large language models and knowledge graphs: Opportunities and challenges
Jeff Z Pan, Simon Razniewski, Jan-Christoph Kalo, Sneha Singhania, Jiaoyan Chen, Stefan Dietze, Hajira Jabeen, Janna Omeliyanenko, Wen Zhang, Matteo Lissandrini, et al · 2023
Cited alongside, same era.
Weizhi Wang, Li Dong, Hao Cheng, Xiaodong Liu, Xifeng Yan, Jianfeng Gao, and Furu Wei · 2023
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Toolformer: Language models can teach themselves to use tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom · 2023
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Visual programming: Compositional visual reasoning without training
Tanmay Gupta and Aniruddha Kembhavi · 2023
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Genegpt: Teaching large language models to use ncbi web apis
Qiao Jin, Yifan Yang, Qingyu Chen, and Zhiyong Lu · 2023
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Explanations as features: Llm-based features for text-attributed graphs
Xiaoxin He, Xavier Bresson, Thomas Laurent, and Bryan Hooi · 2023
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Natural language is all a graph needs
Ruosong Ye, Caiqi Zhang, Runhui Wang, Shuyuan Xu, and Yongfeng Zhang · 2023
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Graphtext: Graph reasoning in text space
Jianan Zhao, Le Zhuo, Yikang Shen, Meng Qu, Kai Liu, Michael Bronstein, Zhaocheng Zhu, and Jian Tang · 2023
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Graphllm: Boosting graph reasoning ability of large language model
Ziwei Chai, Tianjie Zhang, Liang Wu, Kaiqiao Han, Xiaohai Hu, Xuanwen Huang, and Yang Yang · 2023
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Building a knowledge graph to enable precision medicine
Payal Chandak, Kexin Huang, and Marinka Zitnik · 2023
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A survey of hallucination in large foundation models
Vipula Rawte, Amit Sheth, and Amitava Das · 2023
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Autogen: Enabling next-gen llm applications via multi-agent conversation framework
Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, and Chi Wang · 2023
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Heterogeneous graph attention network
Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, and Philip S Yu · 2032
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