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The task of fully inductive link prediction in knowledge graphs has gained significant attention, with various graph neural networks being proposed to address it.
Meta-learning based knowledge extrapolation for knowledge graphs in the federated setting
Mingyang Chen, Wen Zhang, Zhen Yao, Xiangnan Chen, Mengxiao Ding, Fei Huang, and Huajun Chen · 1972
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Signature verification using a" siamese" time delay neural network
Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard Säckinger, and Roopak Shah · 1993
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Sampling from large graphs
Jure Leskovec and Christos Faloutsos · 2006
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Modelling relational data using bayesian clustered tensor factorization
Ilya Sutskever, Joshua Tenenbaum, and Russ R Salakhutdinov · 2009
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Causality
Judea Pearl · 2009
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Relational retrieval using a combination of path-constrained random walks
Ni Lao and William W Cohen · 2010
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A three-way model for collective learning on multi-relational data
Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel · 2011
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Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko · 2013
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Amie: association rule mining under incomplete evidence in ontological knowledge bases
Luis Antonio Galárraga, Christina Teflioudi, Katja Hose, and Fabian Suchanek · 2013
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Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun · 2013
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Wikidata: a free collaborative knowledgebase
Denny Vrandečić and Markus Krötzsch · 2014
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Knowledge graph embedding by translating on hyperplanes
Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen · 2014
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Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld · 2014
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Embedding entities and relations for learning and inference in knowledge bases
Bishan Yang, Scott Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng · 2015
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Dbpedia–a large-scale, multilingual knowledge base extracted from wikipedia
Jens Lehmann, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas, Pablo N Mendes, Sebastian Hellmann, Mohamed Morsey, Patrick Van Kleef, Sören Auer, et al · 2015
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Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Observed versus latent features for knowledge base and text inference
Kristina Toutanova and Danqi Chen · 2015
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Yago3: A knowledge base from multilingual wikipedias
Farzaneh Mahdisoltani, Joanna Asia Biega, and Fabian M. Suchanek · 2015
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Yago: A multilingual knowledge base from wikipedia, wordnet, and geonames
Thomas Rebele, Fabian Suchanek, Johannes Hoffart, Joanna Biega, Erdal Kuzey, and Gerhard Weikum · 2016
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Complex embeddings for simple link prediction
Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard · 2016
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Holographic embeddings of knowledge graphs
Maximilian Nickel, Lorenzo Rosasco, and Tomaso Poggio · 2016
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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola · 2017
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Differentiable learning of logical rules for knowledge base reasoning
Fan Yang, Zhilin Yang, and William W Cohen · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Semi-supervised classification with graph convolutional networks
Thomas Kipf and Max Welling · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl · 2017
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Knowledge graph completion via complex tensor factorization
T Trouillon, CR Dance, E Gaussier, J Welbl, S Riedel, and G Bouchard · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Geometric deep learning: going beyond euclidean data
Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Deeppath: A reinforcement learning method for knowledge graph reasoning
Wenhan Xiong, Thi-Lan-Giao Hoang, and William Yang Wang · 2017
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Challenges and innovations in building a product knowledge graph
Xin Luna Dong · 2018
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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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Link prediction based on graph neural networks
Muhan Zhang and Yixin Chen · 2018
Cited alongside, same era.
Fine-grained evaluation of rule-and embedding-based systems for knowledge graph completion
Christian Meilicke, Manuel Fink, Yanjie Wang, Daniel Ruffinelli, Rainer Gemulla, and Heiner Stuckenschmidt · 2018
Cited alongside, same era.
Convolutional 2d knowledge graph embeddings
Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel · 2018
Cited alongside, same era.
One-shot relational learning for knowledge graphs
Wenhan Xiong, Mo Yu, Shiyu Chang, Xiaoxiao Guo, and William Yang Wang · 2018
Cited alongside, same era.
Neural relational inference for interacting systems
Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, and Richard Zemel · 2018
Neural bellman-ford networks: A general graph neural network framework for link prediction
Zhaocheng Zhu, Zuobai Zhang, Louis-Pascal Xhonneux, and Jian Tang · 2021
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Labeling trick: A theory of using graph neural networks for multi-node representation learning
Muhan Zhang, Pan Li, Yinglong Xia, Kai Wang, and Long Jin · 2021
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How attentive are graph attention networks?
Shaked Brody, Uri Alon, and Eran Yahav · 2021
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Ontozsl: Ontology-enhanced zero-shot learning
Yuxia Geng, Jiaoyan Chen, Zhuo Chen, Jeff Z. Pan, Zhiquan Ye, Zonggang Yuan, Yantao Jia, and Huajun Chen · 2021
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Topology-aware correlations between relations for inductive link prediction in knowledge graphs
Jiajun Chen, Huarui He, Feng Wu, and Jie Wang · 2021
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Tensor field networks: Rotation- and translation-equivariant neural networks for 3d point clouds
Nathaniel Thomas, Tess E. Smidt, Steven M. Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick F. Riley · 2018
Cited alongside, same era.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
Cited alongside, same era.
Drum: End-to-end differentiable rule mining on knowledge graphs
Ali Sadeghian, Mohammadreza Armandpour, Patrick Ding, and Daisy Zhe Wang · 2019
Cited alongside, same era.
Weisfeiler and leman go neural: Higher-order graph neural networks
Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe · 2019
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Position-aware graph neural networks
Jiaxuan You, Rex Ying, and Jure Leskovec · 2019
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Relational pooling for graph representations
Ryan Murphy, Balasubramaniam Srinivasan, Vinayak Rao, and Bruno Ribeiro · 2019
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Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 2021
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Cloudbank: Managed services to simplify cloud access for computer science research and education
Michael Norman, Vince Kellen, Shava Smallen, Brian DeMeulle, Shawn Strande, Ed Lazowska, Naomi Alterman, Rob Fatland, Sarah Stone, Amanda Tan, Katherine Yelick, Eric Van Dusen, and James Mitchell · 2021
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Random features strengthen graph neural networks
Ryoma Sato, Makoto Yamada, and Hisashi Kashima · 2021
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E (n) equivariant graph neural networks
Vıctor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 2021
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Pre-training molecular graph representation with 3d geometry
Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, and Jian Tang · 2021
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Size-invariant graph representations for graph classification extrapolations
Beatrice Bevilacqua, Yangze Zhou, and Bruno Ribeiro · 2021
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The largest knowledge graph in materials science-entities, relations, and link prediction through graph representation learning
Vineeth Venugopal, Sumit Pai, and Elsa Olivetti · 2022
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A survey on negative transfer
Wen Zhang, Lingfei Deng, Lei Zhang, and Dongrui Wu · 2022
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Few-shot relational reasoning via connection subgraph pretraining
Qian Huang, Hongyu Ren, and Jure Leskovec · 2022
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Inductive relation prediction using analogy subgraph embeddings
Jiarui Jin, Yangkun Wang, Kounianhua Du, Weinan Zhang, Zheng Zhang, David Wipf, Yong Yu, and Quan Gan · 2022
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Inductive relation prediction by bert
Hanwen Zha, Zhiyu Chen, and Xifeng Yan · 2022
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Neural-symbolic models for logical queries on knowledge graphs
Zhaocheng Zhu, Mikhail Galkin, Zuobai Zhang, and Jian Tang · 2022
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Weisfeiler and leman go relational
Pablo Barcelo, Mikhail Galkin, Christopher Morris, and Miguel Romero Orth · 2022
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Rlogic: Recursive logical rule learning from knowledge graphs
Kewei Cheng, Jiahao Liu, Wei Wang, and Yizhou Sun · 2022
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Inductive logical query answering in knowledge graphs
Mikhail Galkin, Zhaocheng Zhu, Hongyu Ren, and Jian Tang · 2022
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Geometrically equivariant graph neural networks: A survey
Jiaqi Han, Yu Rong, Tingyang Xu, and Wenbing Huang · 2022
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Learning from counterfactual links for link prediction
Tong Zhao, Gang Liu, Daheng Wang, Wenhao Yu, and Meng Jiang · 2022
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Building a knowledge graph to enable precision medicine
Payal Chandak, Kexin Huang, and Marinka Zitnik · 2023
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The materials experiment knowledge graph
Michael J Statt, Brian A Rohr, Dan Guevarra, Santosh K Suram, John M Gregoire, et al · 2023
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InGram: Inductive knowledge graph embedding via relation graphs
Jaejun Lee, Chanyoung Chung, and Joyce Jiyoung Whang · 2023
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Towards foundation models for knowledge graph reasoning
Mikhail Galkin, Xinyu Yuan, Hesham Mostafa, Jian Tang, and Zhaocheng Zhu · 2023
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Mitigating negative transfer for better generalization and efficiency in transfer learning
Zirui Wang · 2023
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Relational message passing for fully inductive knowledge graph completion
Yuxia Geng, Jiaoyan Chen, Jeff Z Pan, Mingyang Chen, Song Jiang, Wen Zhang, and Huajun Chen · 2023
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Logical expressiveness of graph neural network for knowledge graph reasoning
Haiquan Qiu, Yongqi Zhang, Yong Li, and Quanming Yao · 2023
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Generalizing to unseen elements: A survey on knowledge extrapolation for knowledge graphs
Mingyang Chen, Wen Zhang, Yuxia Geng, Zezhong Xu, Jeff Z Pan, and Huajun Chen · 2023
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Uni-mol: A universal 3d molecular representation learning framework
Gengmo Zhou, Zhifeng Gao, Qiankun Ding, Hang Zheng, Hongteng Xu, Zhewei Wei, Linfeng Zhang, and Guolin Ke · 2023
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Haitao Mao, Zhikai Chen, Wenzhuo Tang, Jianan Zhao, Yao Ma, Tong Zhao, Neil Shah, Michael Galkin, and Jiliang Tang · 2024
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Uncovering neural scaling laws in molecular representation learning
Dingshuo Chen, Yanqiao Zhu, Jieyu Zhang, Yuanqi Du, Zhixun Li, Qiang Liu, Shu Wu, and Liang Wang · 2024
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Prodigy: Enabling in-context learning over graphs
Qian Huang, Hongyu Ren, Peng Chen, Gregor Kržmanc, Daniel Zeng, Percy S Liang, and Jure Leskovec · 2024
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