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Graph-structured data pervades domains such as social networks, biological systems, knowledge graphs, and recommender systems.
An appraisal of some shortest-path algorithms
Stuart E Dreyfus · 1969
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Long short-term memory
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The shapley value
Eyal Winter · 2002
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Integrating structured biological data by kernel maximum mean discrepancy
Karsten M Borgwardt, Arthur Gretton, Malte J Rasch, Hans-Peter Kriegel, Bernhard Schölkopf, and Alex J Smola · 2006
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A tutorial on spectral clustering
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Graph theory
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Graph kernels
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Principal component analysis
Hervé Abdi and Lynne J Williams · 2010
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A comparative study of training algorithms for supervised machine learning
Hetal Bhavsar and Amit Ganatra · 2012
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Long short-term memory
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Deep learning of representations for unsupervised and transfer learning
Yoshua Bengio · 2012
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Auto-encoding variational bayes
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Empirical evaluation of gated recurrent neural networks on sequence modeling
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio · 2014
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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Line: Large-scale information network embedding
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
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Variational autoencoder for deep learning of images, labels and captions
Yunchen Pu, Zhe Gan, Ricardo Henao, Xin Yuan, Chunyuan Li, Andrew Stevens, and Lawrence Carin · 2016
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Variational graph auto-encoders
Thomas N Kipf and Max Welling · 2016
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The value of semantic parse labeling for knowledge base question answering
Wen-tau Yih, Matthew Richardson, Christopher Meek, Ming-Wei Chang, and Jina Suh · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 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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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Disentangled representation learning gan for pose-invariant face recognition
Luan Tran, Xi Yin, and Xiaoming Liu · 2017
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Feature engineering for machine learning and data analytics
Guozhu Dong and Huan Liu · 2018
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Graphrnn: Generating realistic graphs with deep auto-regressive models
Jiaxuan You, Rex Ying, Xiang Ren, William Hamilton, and Jure Leskovec · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio · 2018
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Deep clustering for unsupervised learning of visual features
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 2018
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Processing of missing data by neural networks
Marek Śmieja, Łukasz Struski, Jacek Tabor, Bartosz Zieliński, and Przemysław Spurek · 2018
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Graphvae: Towards generation of small graphs using variational autoencoders
Martin Simonovsky and Nikos Komodakis · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Molecularrnn: Generating realistic molecular graphs with optimized properties
Mariya Popova, Mykhailo Shvets, Junier Oliva, and Olexandr Isayev · 2019
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Deep graph infomax
Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm · 2019
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Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio · 2019
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Graph-based knowledge distillation by multi-head attention network
Seunghyun Lee and Byung Cheol Song · 2019
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Graph representation learning via multi-task knowledge distillation
Jiaqi Ma and Qiaozhu Mei · 2019
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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych · 2019
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Gqa: A new dataset for real-world visual reasoning and compositional question answering
Drew A Hudson and Christopher D Manning · 2019
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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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On the transferability of spectral graph filters
Ron Levie, Elvin Isufi, and Gitta Kutyniok · 2019
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Deepgcns: Can gcns go as deep as cnns?
Guohao Li, Matthias Muller, Ali Thabet, and Bernard Ghanem · 2019
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Towards efficient data valuation based on the shapley value
Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nick Hynes, Nezihe Merve Gürel, Bo Li, Ce Zhang, Dawn Song, and Costas J Spanos · 2019
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Machine learning algorithms-a review
Batta Mahesh et al · 2020
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A comprehensive survey on transfer learning
Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, and Qing He · 2020
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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A review: Knowledge reasoning over knowledge graph
Xiaojun Chen, Shengbin Jia, and Yang Xiang · 2020
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A generalization of transformer networks to graphs
Vijay Prakash Dwivedi and Xavier Bresson · 2020
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Gpt-gnn: Generative pre-training of graph neural networks
Ziniu Hu, Yuxiao Dong, Kuansan Wang, Kai-Wei Chang, and Yizhou Sun · 2020
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Edge-based sequential graph generation with recurrent neural networks
Davide Bacciu, Alessio Micheli, and Marco Podda · 2020
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Graphgen: A scalable approach to domain-agnostic labeled graph generation
Nikhil Goyal, Harsh Vardhan Jain, and Sayan Ranu · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Bootstrap your own latent-a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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Contrastive multiview coding
Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2020
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Understanding contrastive representation learning through alignment and uniformity on the hypersphere
Tongzhou Wang and Phillip Isola · 2020
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What makes for good views for contrastive learning?
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola · 2020
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Supervised contrastive learning
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan · 2020
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Graph contrastive learning with augmentations
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen · 2020
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Deep graph contrastive representation learning
Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang · 2020
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Contrastive multi-view representation learning on graphs
Kaveh Hassani and Amir Hosein Khasahmadi · 2020
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Self-supervised learning on graphs: Deep insights and new direction
Wei Jin, Tyler Derr, Haochen Liu, Yiqi Wang, Suhang Wang, Zitao Liu, and Jiliang Tang · 2020
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Sub-graph contrast for scalable self-supervised graph representation learning
Yizhu Jiao, Yun Xiong, Jiawei Zhang, Yao Zhang, Tianqi Zhang, and Yangyong Zhu · 2020
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Tent: Fully test-time adaptation by entropy minimization
Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell · 2020
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Generalizing from a few examples: A survey on few-shot learning
Yaqing Wang, Quanming Yao, James T Kwok, and Lionel M Ni · 2020
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Gcc: Graph contrastive coding for graph neural network pre-training
Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, and Jie Tang · 2020
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Generalization and representational limits of graph neural networks
Vikas Garg, Stefanie Jegelka, and Tommi Jaakkola · 2020
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Can graph neural networks count substructures?
Zhengdao Chen, Lei Chen, Soledad Villar, and Joan Bruna · 2020
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Geom-gcn: Geometric graph convolutional networks
Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang · 2020
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Self-supervised graph transformer on large-scale molecular data
Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, and Junzhou Huang · 2020
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Self-supervised auxiliary learning with meta-paths for heterogeneous graphs
Dasol Hwang, Jinyoung Park, Sunyoung Kwon, KyungMin Kim, Jung-Woo Ha, and Hyunwoo J Kim · 2020
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Graphon neural networks and the transferability of graph neural networks
Luana Ruiz, Luiz Chamon, and Alejandro Ribeiro · 2020
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Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
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Machine learning
Ethem Alpaydin · 2021
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Self-supervised learning: Generative or contrastive
Xiao Liu, Fanjin Zhang, Zhenyu Hou, Li Mian, Zhaoyu Wang, Jing Zhang, and Jie Tang · 2021
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On the opportunities and risks of foundation models
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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Florence: A new foundation model for computer vision
Lu Yuan, Dongdong Chen, Yi-Ling Chen, Noel Codella, Xiyang Dai, Jianfeng Gao, Houdong Hu, Xuedong Huang, Boxin Li, Chunyuan Li, et al · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
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Understanding over-squashing and bottlenecks on graphs via curvature
Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, and Michael M Bronstein · 2021
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Self-supervised learning on graphs: Contrastive, generative, or predictive
Lirong Wu, Haitao Lin, Cheng Tan, Zhangyang Gao, and Stan Z Li · 2021
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Graph contrastive learning with adaptive augmentation
Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang · 2021
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Large-scale representation learning on graphs via bootstrapping
Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Mehdi Azabou, Eva L Dyer, Remi Munos, Petar Veličković, and Michal Valko · 2021
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Adaptive transfer learning on graph neural networks
Xueting Han, Zhenhuan Huang, Bang An, and Jing Bai · 2021
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Knowledge distillation: A survey
Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao · 2021
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Graph-less neural networks: Teaching old mlps new tricks via distillation
Shichang Zhang, Yozen Liu, Yizhou Sun, and Neil Shah · 2021
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Multi-task self-distillation for graph-based semi-supervised learning
Yating Ren, Junzhong Ji, Lingfeng Niu, and Minglong Lei · 2021
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Transfer learning of graph neural networks with ego-graph information maximization
Qi Zhu, Carl Yang, Yidan Xu, Haonan Wang, Chao Zhang, and Jiawei Han · 2021
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Graph convolutional networks for graphs containing missing features
Hibiki Taguchi, Xin Liu, and Tsuyoshi Murata · 2021
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Perceiver io: A general architecture for structured inputs & outputs
Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, et al · 2021
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Dirichlet energy constrained learning for deep graph neural networks
Kaixiong Zhou, Xiao Huang, Daochen Zha, Rui Chen, Li Li, Soo-Hyun Choi, and Xia Hu · 2021
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Cross-network learning with partially aligned graph convolutional networks
Meng Jiang · 2021
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Graphformers: Gnn-nested transformers for representation learning on textual graph
Junhan Yang, Zheng Liu, Shitao Xiao, Chaozhuo Li, Defu Lian, Sanjay Agrawal, Amit Singh, Guangzhong Sun, and Xing Xie · 2021
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Learning to pre-train graph neural networks
Yuanfu Lu, Xunqiang Jiang, Yuan Fang, and Chuan Shi · 2021
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Pre-training graph neural network for cross domain recommendation
Chen Wang, Yueqing Liang, Zhiwei Liu, Tao Zhang, and S Yu Philip · 2021
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Cross-domain graph anomaly detection
Kaize Ding, Kai Shu, Xuan Shan, Jundong Li, and Huan Liu · 2021
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Explagraphs: An explanation graph generation task for structured commonsense reasoning
Swarnadeep Saha, Prateek Yadav, Lisa Bauer, and Mohit Bansal · 2021
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Text2mol: Cross-modal molecule retrieval with natural language queries
Carl Edwards, ChengXiang Zhai, and Heng Ji · 2021
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Physics-inspired structural representations for molecules and materials
Felix Musil, Andrea Grisafi, Albert P Bartók, Christoph Ortner, Gábor Csányi, and Michele Ceriotti · 2021
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Pre-training on large-scale heterogeneous graph
Xunqiang Jiang, Tianrui Jia, Yuan Fang, Chuan Shi, Zhe Lin, and Hui Wang · 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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Vicreg: Variance-invariance-covariance regularization for self-supervised learning
Adrien Bardes, Jean Ponce, and Yann LeCun · 2021
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Transferability of spectral graph convolutional neural networks
Ron Levie, Wei Huang, Lorenzo Bucci, Michael Bronstein, and Gitta Kutyniok · 2021
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On the bottleneck of graph neural networks and its practical implications
Uri Alon and Eran Yahav · 2021
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Dgcl: An efficient communication library for distributed gnn training
Zhenkun Cai, Xiao Yan, Yidi Wu, Kaihao Ma, James Cheng, and Fan Yu · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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A graph placement methodology for fast chip design
Azalia Mirhoseini, Anna Goldie, Mustafa Yazgan, Joe Wenjie Jiang, Ebrahim Songhori, Shen Wang, Young-Joon Lee, Eric Johnson, Omkar Pathak, Azade Nova, et al · 2021
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Graph self-supervised learning: A survey
Yixin Liu, Ming Jin, Shirui Pan, Chuan Zhou, Yu Zheng, Feng Xia, and S Yu Philip · 2022
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Emergent abilities of large language models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al · 2022
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Not too little, not too much: a theoretical analysis of graph (over) smoothing
Nicolas Keriven · 2022
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Is homophily a necessity for graph neural networks?
Yao Ma, Xiaorui Liu, Neil Shah, and Jiliang Tang · 2022
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Gppt: Graph pre-training and prompt tuning to generalize graph neural networks
Mingchen Sun, Kaixiong Zhou, Xin He, Ying Wang, and Xin Wang · 2022
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Graphbert: Bridging graph and text for malicious behavior detection on social media
Jiele Wu, Chunhui Zhang, Zheyuan Liu, Erchi Zhang, Steven Wilson, and Chuxu Zhang · 2022
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Recipe for a general, powerful, scalable graph transformer
Ladislav Rampasek, Mikhail Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, and Dominique Beaini · 2022
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A survey on in-context learning
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Tianyu Liu, et al · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2022
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Learning on large-scale text-attributed graphs via variational inference
Jianan Zhao, Meng Qu, Chaozhuo Li, Hao Yan, Qian Liu, Rui Li, Xing Xie, and Jian Tang · 2022
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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Graphmae: Self-supervised masked graph autoencoders
Zhenyu Hou, Xiao Liu, Yukuo Cen, Yuxiao Dong, Hongxia Yang, Chunjie Wang, and Jie Tang · 2022
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Large-scale representation learning on graphs via bootstrapping
Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Mehdi Azabou, Eva L Dyer, Remi Munos, Petar Veličković, and Michal Valko · 2022
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On representation knowledge distillation for graph neural networks
Chaitanya K Joshi, Fayao Liu, Xu Xun, Jie Lin, and Chuan Sheng Foo · 2022
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Learning mlps on graphs: A unified view of effectiveness, robustness, and efficiency
Yijun Tian, Chuxu Zhang, Zhichun Guo, Xiangliang Zhang, and Nitesh Chawla · 2022
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Knowledge distillation improves graph structure augmentation for graph neural networks
Lirong Wu, Haitao Lin, Yufei Huang, and Stan Z Li · 2022
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Contrastive test-time adaptation
Dian Chen, Dequan Wang, Trevor Darrell, and Sayna Ebrahimi · 2022
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Parameter-free online test-time adaptation
Malik Boudiaf, Romain Mueller, Ismail Ben Ayed, and Luca Bertinetto · 2022
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Empowering graph representation learning with test-time graph transformation
Wei Jin, Tong Zhao, Jiayuan Ding, Yozen Liu, Jiliang Tang, and Neil Shah · 2022
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Chasing all-round graph representation robustness: Model, training, and optimization
Chunhui Zhang, Yijun Tian, Mingxuan Ju, Zheyuan Liu, Yanfang Ye, Nitesh Chawla, and Chuxu Zhang · 2022
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Graph neural networks in node classification: survey and evaluation
Shunxin Xiao, Shiping Wang, Yuanfei Dai, and Wenzhong Guo · 2022
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Co-modality graph contrastive learning for imbalanced node classification
Yiyue Qian, Chunhui Zhang, Yiming Zhang, Qianlong Wen, Yanfang Ye, and Chuxu Zhang · 2022
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A survey on heterogeneous graph embedding: methods, techniques, applications and sources
Xiao Wang, Deyu Bo, Chuan Shi, Shaohua Fan, Yanfang Ye, and Philip S Yu · 2022
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A survey of explainable graph neural networks: Taxonomy and evaluation metrics
Yiqiao Li, Jianlong Zhou, Sunny Verma, and Fang Chen · 2022
Earlier work this paper cites.
Fine-tuning graph neural networks via graph topology induced optimal transport
Jiying Zhang, Xi Xiao, Long-Kai Huang, Yu Rong, and Yatao Bian · 2022
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Cross-domain few-shot graph classification
Kaveh Hassani · 2022
Earlier work this paper cites.
Digress: Discrete denoising diffusion for graph generation
Clement Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, and Pascal Frossard · 2022
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Score-based generative modeling of graphs via the system of stochastic differential equations
Jaehyeong Jo, Seul Lee, and Sung Ju Hwang · 2022
Cited alongside, same era.
Graph anomaly detection with graph neural networks: Current status and challenges
Hwan Kim, Byung Suk Lee, Won-Yong Shin, and Sungsu Lim · 2022
Cited alongside, same era.
Galactica: A large language model for science
Ross Taylor, Marcin Kardas, Guillem Cucurull, Thomas Scialom, Anthony Hartshorn, Elvis Saravia, Andrew Poulton, Viktor Kerkez, and Robert Stojnic · 2022
Cited alongside, same era.
Language models of protein sequences at the scale of evolution enable accurate structure prediction
Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido, et al · 2022
Cited alongside, same era.
A molecular multimodal foundation model associating molecule graphs with natural language
Bing Su, Dazhao Du, Zhao Yang, Yujie Zhou, Jiangmeng Li, Anyi Rao, Hao Sun, Zhiwu Lu, and Ji-Rong Wen · 2022
Cited alongside, same era.
Hgprompt: Bridging homogeneous and heterogeneous graphs for few-shot prompt learning
Xingtong Yu, Yuan Fang, Zemin Liu, and Xinming Zhang · 2024
Later among the works it cites.
Towards graph foundation models: The perspective of zero-shot reasoning on knowledge graphs
Kai Wang and Siqiang Luo · 2024
Later among the works it cites.
Zero-shot generalization of GNNs over distinct attribute domains
Yangyi Shen, Jincheng Zhou, Beatrice Bevilacqua, Joshua Robinson, Charilaos Kanatsoulis, Jure Leskovec, and Bruno Ribeiro · 2024
Later among the works it cites.
Boosting graph foundation model from structural perspective
Yao Cheng, Yige Zhao, Jianxiang Yu, and Xiang Li · 2024
Later among the works it cites.
Uniglm: Training one unified language model for text-attributed graphs
Yi Fang, Dongzhe Fan, Sirui Ding, Ninghao Liu, and Qiaoyu Tan · 2024
Later among the works it cites.
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3d infomax improves gnns for molecular property prediction
Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, and Pietro Liò · 2022
Cited alongside, same era.
Molecular contrastive learning of representations via graph neural networks
Yuyang Wang, Jianren Wang, Zhonglin Cao, and Amir Barati Farimani · 2022
Cited alongside, same era.
A generalist neural algorithmic learner
Borja Ibarz, Vitaly Kurin, George Papamakarios, Kyriacos Nikiforou, Mehdi Bennani, Róbert Csordás, Andrew Joseph Dudzik, Matko Bošnjak, Alex Vitvitskyi, Yulia Rubanova, et al · 2022
Cited alongside, same era.
Few-shot heterogeneous graph learning via cross-domain knowledge transfer
Qiannan Zhang, Xiaodong Wu, Qiang Yang, Chuxu Zhang, and Xiangliang Zhang · 2022
Cited alongside, same era.
Transferability in deep learning: A survey
Junguang Jiang, Yang Shu, Jianmin Wang, and Mingsheng Long · 2022
Cited alongside, same era.
Long range graph benchmark
Vijay Prakash Dwivedi, Ladislav Rampášek, Mikhail Galkin, Ali Parviz, Guy Wolf, Anh Tuan Luu, and Dominique Beaini · 2022
Cited alongside, same era.
Gpt-4 technical report
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
Cited alongside, same era.
Zhenyu Hou, Haozhan Li, Yukuo Cen, Jie Tang, and Yuxiao Dong · 2024
Later among the works it cites.
One model for one graph: A new perspective for pretraining with cross-domain graphs
Jingzhe Liu, Haitao Mao, Zhikai Chen, Wenqi Fan, Mingxuan Ju, Tong Zhao, Neil Shah, and Jiliang Tang · 2024
Later among the works it cites.
Generalizing graph transformers across diverse graphs and tasks via pre-training on industrial-scale data
Yufei He, Zhenyu Hou, Yukuo Cen, Feng He, Xu Cheng, and Bryan Hooi · 2024
Later among the works it cites.
Multigprompt for multi-task pre-training and prompting on graphs
Xingtong Yu, Chang Zhou, Yuan Fang, and Xinming Zhang · 2024
Later among the works it cites.
Ragraph: A general retrieval-augmented graph learning framework
Xinke Jiang, Rihong Qiu, Yongxin Xu, Wentao Zhang, Yichen Zhu, Ruizhe Zhang, Yuchen Fang, Xu Chu, Junfeng Zhao, and Yasha Wang · 2024
Later among the works it cites.
Beyond weisfeiler-lehman: A quantitative framework for gnn expressiveness
Bohang Zhang, Jingchu Gai, Yiheng Du, Qiwei Ye, Di He, and Liwei Wang · 2024
Later among the works it cites.
Ugmae: A unified framework for graph masked autoencoders
Yijun Tian, Chuxu Zhang, Ziyi Kou, Zheyuan Liu, Xiangliang Zhang, and Nitesh V Chawla · 2024
Later among the works it cites.
Graphagent: Exploiting large language models for interpretable learning on text-attributed graphs, 2024
Xinmiao Yu, Meng Qu, Xiaocheng Feng, and Bing Qin · 2024
Later among the works it cites.
Instructgraph: Boosting large language models via graph-centric instruction tuning and preference alignment
Jianing Wang, Junda Wu, Yupeng Hou, Yao Liu, Ming Gao, and Julian McAuley · 2024
Later among the works it cites.
Graphgpt: Graph instruction tuning for large language models
Jiabin Tang, Yuhao Yang, Wei Wei, Lei Shi, Lixin Su, Suqi Cheng, Dawei Yin, and Chao Huang · 2024
Later among the works it cites.
Enhance graph alignment for large language models
Haitong Luo, Xuying Meng, Suhang Wang, Tianxiang Zhao, Fali Wang, Hanyun Cao, and Yujun Zhang · 2024
Later among the works it cites.
LLM as GNN: Graph vocabulary learning for graph foundation model, 2024
Xi Zhu, Haochen Xue, Ziwei Zhao, Mingyu Jin, Wujiang Xu, Jingyuan Huang, Qifan Wang, Kaixiong Zhou, and Yongfeng Zhang · 2024
Later among the works it cites.
Can we soft prompt llms for graph learning tasks?
Zheyuan Liu, Xiaoxin He, Yijun Tian, and Nitesh V Chawla · 2024
Later among the works it cites.
Llms as zero-shot graph learners: Alignment of gnn representations with llm token embeddings
Duo Wang, Yuan Zuo, Fengzhi Li, and Junjie Wu · 2024
Later among the works it cites.
Nt-llm: A novel node tokenizer for integrating graph structure into large language models
Yanbiao Ji, Chang Liu, Xin Chen, Yue Ding, Dan Luo, Mei Li, Wenqing Lin, and Hongtao Lu · 2024
Later among the works it cites.
Graphagent: Agentic graph language assistant
Yuhao Yang, Jiabin Tang, Lianghao Xia, Xingchen Zou, Yuxuan Liang, and Chao Huang · 2024
Later among the works it cites.
Efficient tuning and inference for large language models on textual graphs
Yun Zhu, Yaoke Wang, Haizhou Shi, and Siliang Tang · 2024
Later among the works it cites.
All against some: Efficient integration of large language models for message passing in graph neural networks
Ajay Jaiswal, Nurendra Choudhary, Ravinarayana Adkathimar, Muthu P Alagappan, Gaurush Hiranandani, Ying Ding, Zhangyang Wang, Edward W Huang, and Karthik Subbian · 2024
Later among the works it cites.
GraphFM: A Scalable Framework for Multi-Graph Pretraining
Divyansha Lachi, Mehdi Azabou, Vinam Arora, and Eva Dyer · 2024
Later among the works it cites.
Replay-and-forget-free graph class-incremental learning: A task profiling and prompting approach
Chaoxi Niu, Guansong Pang, Ling Chen, and Bing Liu · 2024
Later among the works it cites.
Graphcontrol: Adding conditional control to universal graph pre-trained models for graph domain transfer learning
Yun Zhu, Yaoke Wang, Haizhou Shi, Zhenshuo Zhang, Dian Jiao, and Siliang Tang · 2024
Later among the works it cites.
A pure transformer pretraining framework on text-attributed graphs
Yu Song, Haitao Mao, Jiachen Xiao, Jingzhe Liu, Zhikai Chen, Wei Jin, Carl Yang, Jiliang Tang, and Hui Liu · 2024
Later among the works it cites.
Login: A large language model consulted graph neural network training framework
Yiran Qiao, Xiang Ao, Yang Liu, Jiarong Xu, Xiaoqian Sun, and Qing He · 2024
Later among the works it cites.
Bridging large language models and graph structure learning models for robust representation learning
Guangxin Su, Yifan Zhu, Wenjie Zhang, Hanchen Wang, and Ying Zhang · 2024
Later among the works it cites.
Cost-effective label-free node classification with llms
Taiyan Zhang, Renchi Yang, Mingyu Yan, Xiaochun Ye, Dongrui Fan, and Yurui Lai · 2024
Later among the works it cites.
LangTopo: Aligning Language Descriptions of Graphs with Tokenized Topological Modeling
Zhong Guan, Hongke Zhao, Likang Wu, Ming He, and Jianpin Fan · 2024
Later among the works it cites.
Multi-view empowered structural graph wordification for language models
Zipeng Liu, Likang Wu, Ming He, Zhong Guan, Hongke Zhao, and Nan Feng · 2024
Later among the works it cites.
Position: Graph foundation models are already here
Haitao Mao, Zhikai Chen, Wenzhuo Tang, Jianan Zhao, Yao Ma, Tong Zhao, Neil Shah, Mikhail Galkin, and Jiliang Tang · 2024
Later among the works it cites.
Fug: Feature-universal graph contrastive pre-training for graphs with diverse node features
Jitao Zhao, Di Jin, Meng Ge, Lianze Shan, Xin Wang, Dongxiao He, and Zhiyong Feng · 2024
Later among the works it cites.
Robust node classification on graph data with graph and label noise
Yonghua Zhu, Lei Feng, Zhenyun Deng, Yang Chen, Robert Amor, and Michael Witbrock · 2024
Later among the works it cites.
Universal link predictor by in-context learning on graphs
Kaiwen Dong, Haitao Mao, Zhichun Guo, and Nitesh V Chawla · 2024
Later among the works it cites.
Domain-adaptive graph attention-supervised network for cross-network edge classification
Xiao Shen, Mengqiu Shao, Shirui Pan, Laurence T. Yang, and Xi Zhou · 2024
Later among the works it cites.
G-adapter: Towards structure-aware parameter-efficient transfer learning for graph transformer networks
Anchun Gui, Jinqiang Ye, and Han Xiao · 2024
Later among the works it cites.
Fine-tuning graph neural networks by preserving graph generative patterns
Yifei Sun, Qi Zhu, Yang Yang, Chunping Wang, Tianyu Fan, Jiajun Zhu, and Lei Chen · 2024
Later among the works it cites.
Towards foundation models on graphs: An analysis on cross-dataset transfer of pretrained gnns
Fabrizio Frasca, Fabian Jogl, Moshe Eliasof, Matan Ostrovsky, Carola-Bibiane Schönlieb, Thomas Gärtner, and Haggai Maron · 2024
Later among the works it cites.
A graph is worth k k words: Euclideanizing graph using pure transformer
Zhangyang Gao, Daize Dong, Cheng Tan, Jun Xia, Bozhen Hu, and Stan Z Li · 2024
Later among the works it cites.
Exploring the potential of large language models in graph generation
Yang Yao, Xin Wang, Zeyang Zhang, Yijian Qin, Ziwei Zhang, Xu Chu, Yuekui Yang, Wenwu Zhu, and Hong Mei · 2024
Later among the works it cites.
Llm and gnn are complementary: Distilling llm for multimodal graph learning
Junjie Xu, Zongyu Wu, Minhua Lin, Xiang Zhang, and Suhang Wang · 2024
Later among the works it cites.
Instructg2i: Synthesizing images from multimodal attributed graphs
Bowen Jin, Ziqi Pang, Bingjun Guo, Yu-Xiong Wang, Jiaxuan You, and Jiawei Han · 2024
Later among the works it cites.
Large generative graph models
Yu Wang, Ryan A Rossi, Namyong Park, Huiyuan Chen, Nesreen K Ahmed, Puja Trivedi, Franck Dernoncourt, Danai Koutra, and Tyler Derr · 2024
Later among the works it cites.
Benchmarking and improving large vision-language models for fundamental visual graph understanding and reasoning
Yingjie Zhu, Xuefeng Bai, Kehai Chen, Yang Xiang, and Min Zhang · 2024
Later among the works it cites.
G-retriever: Retrieval-augmented generation for textual graph understanding and question answering
Xiaoxin He, Yijun Tian, Yifei Sun, Nitesh V Chawla, Thomas Laurent, Yann LeCun, Xavier Bresson, and Bryan Hooi · 2024
Later among the works it cites.
GITA: Graph to Visual and Textual Integration for Vision-Language Graph Reasoning
Yanbin Wei, Shuai Fu, Weisen Jiang, Zejian Zhang, Zhixiong Zeng, Qi Wu, James T. Kwok, and Yu Zhang · 2024
Later among the works it cites.
Llmrec: Large language models with graph augmentation for recommendation
Wei Wei, Xubin Ren, Jiabin Tang, Qinyong Wang, Lixin Su, Suqi Cheng, Junfeng Wang, Dawei Yin, and Chao Huang · 2024
Later among the works it cites.
Towards graph foundation models for personalization
Andreas Damianou, Francesco Fabbri, Paul Gigioli, Marco De Nadai, Alice Wang, Enrico Palumbo, and Mounia Lalmas · 2024
Later among the works it cites.
Representation learning with large language models for recommendation
Xubin Ren, Wei Wei, Lianghao Xia, Lixin Su, Suqi Cheng, Junfeng Wang, Dawei Yin, and Chao Huang · 2024
Later among the works it cites.
Towards cross-domain few-shot graph anomaly detection
Jiazhen Chen, Sichao Fu, Zhibin Zhang, Zheng Ma, Mingbin Feng, Tony S Wirjanto, and Qinmu Peng · 2024
Later among the works it cites.
Zero-shot generalist graph anomaly detection with unified neighborhood prompts
Chaoxi Niu, Hezhe Qiao, Changlu Chen, Ling Chen, and Guansong Pang · 2024
Later among the works it cites.
Graph neural prompting with large language models
Yijun Tian, Huan Song, Zichen Wang, Haozhu Wang, Ziqing Hu, Fang Wang, Nitesh V Chawla, and Panpan Xu · 2024
Later among the works it cites.
Graph neural network enhanced retrieval for question answering of llms
Zijian Li, Qingyan Guo, Jiawei Shao, Lei Song, Jiang Bian, Jun Zhang, and Rui Wang · 2024
Later among the works it cites.
Kg-adapter: Enabling knowledge graph integration in large language models through parameter-efficient fine-tuning
Shiyu Tian, Yangyang Luo, Tianze Xu, Caixia Yuan, Huixing Jiang, Chen Wei, and Xiaojie Wang · 2024
Later among the works it cites.
Guarding graph neural networks for unsupervised graph anomaly detection
Yuanchen Bei, Sheng Zhou, Jinke Shi, Yao Ma, Haishuai Wang, and Jiajun Bu · 2024
Later among the works it cites.
Dpa-2: a large atomic model as a multi-task learner
Duo Zhang, Xinzijian Liu, Xiangyu Zhang, Chengqian Zhang, Chun Cai, Hangrui Bi, Yiming Du, Xuejian Qin, Anyang Peng, Jiameng Huang, et al · 2024
Later among the works it cites.
Towards predicting equilibrium distributions for molecular systems with deep learning
Shuxin Zheng, Jiyan He, Chang Liu, Yu Shi, Ziheng Lu, Weitao Feng, Fusong Ju, Jiaxi Wang, Jianwei Zhu, Yaosen Min, et al · 2024
Later among the works it cites.
Graph transformer foundation model for modeling admet properties
Mikolaj Mizera, Arkadii Lin, Eugene Babin, Yury Kashkur, Tatiana Sitnik, Ien An Chan, Arsen Yedige, Maksim Vendin, Shamkhal Baybekov, and Vladimir Aladinskiy · 2024
Later among the works it cites.
Moleculargpt: Open large language model (llm) for few-shot molecular property prediction
Yuyan Liu, Sirui Ding, Sheng Zhou, Wenqi Fan, and Qiaoyu Tan · 2024
Later among the works it cites.
Gp-gpt: Large language model for gene-phenotype mapping
Yanjun Lyu, Zihao Wu, Lu Zhang, Jing Zhang, Yiwei Li, Wei Ruan, Zhengliang Liu, Xiaowei Yu, Chao Cao, Tong Chen, et al · 2024
Later among the works it cites.
Git-mol: A multi-modal large language model for molecular science with graph, image, and text
Pengfei Liu, Yiming Ren, Jun Tao, and Zhixiang Ren · 2024
Later among the works it cites.
Predicting equilibrium distributions for molecular systems with deep learning
Shuxin Zheng, Jiyan He, Chang Liu, Yu Shi, Ziheng Lu, Weitao Feng, Fusong Ju, Jiaxi Wang, Jianwei Zhu, Yaosen Min, et al · 2024
Later among the works it cites.
Graphtool-instruction: Revolutionizing graph reasoning in llms through decomposed subtask instruction
Rongzheng Wang, Shuang Liang, Qizhi Chen, Jiasheng Zhang, and Ke Qin · 2024
Later among the works it cites.
A hierarchical language model for interpretable graph reasoning
Sambhav Khurana, Xiner Li, Shurui Gui, and Shuiwang Ji · 2024
Later among the works it cites.
Can llms perform structured graph reasoning?
Palaash Agrawal, Shavak Vasania, and Cheston Tan · 2024
Later among the works it cites.
Beyond graphs: Can large language models comprehend hypergraphs?
Yifan Feng, Chengwu Yang, Xingliang Hou, Shaoyi Du, Shihui Ying, Zongze Wu, and Yue Gao · 2024
Later among the works it cites.
Graph linearization methods for reasoning on graphs with large language models
Christos Xypolopoulos, Guokan Shang, Xiao Fei, Giannis Nikolentzos, Hadi Abdine, Iakovos Evdaimon, Michail Chatzianastasis, Giorgos Stamou, and Michalis Vazirgiannis · 2024
Later among the works it cites.
Scalable and accurate graph reasoning with llm-based multi-agents
Yuwei Hu, Runlin Lei, Xinyi Huang, Zhewei Wei, and Yongchao Liu · 2024
Later among the works it cites.
Graphteam: Facilitating large language model-based graph analysis via multi-agent collaboration
Xin Li, Qizhi Chu, Yubin Chen, Yang Liu, Yaoqi Liu, Zekai Yu, Weize Chen, Chen Qian, Chuan Shi, and Cheng Yang · 2024
Later among the works it cites.
Gundam: Aligning large language models with graph understanding
Sheng Ouyang, Yulan Hu, Ge Chen, and Yong Liu · 2024
Later among the works it cites.
Graphinstruct: Empowering large language models with graph understanding and reasoning capability
Zihan Luo, Xiran Song, Hong Huang, Jianxun Lian, Chenhao Zhang, Jinqi Jiang, and Xing Xie · 2024
Later among the works it cites.
Gcoder: Improving large language model for generalized graph problem solving
Qifan Zhang, Xiaobin Hong, Jianheng Tang, Nuo Chen, Yuhan Li, Wenzhong Li, Jing Tang, and Jia Li · 2024
Later among the works it cites.
Let your graph do the talking: Encoding structured data for llms
Bryan Perozzi, Bahare Fatemi, Dustin Zelle, Anton Tsitsulin, Mehran Kazemi, Rami Al-Rfou, and Jonathan Halcrow · 2024
Later among the works it cites.
How do large language models understand graph patterns? a benchmark for graph pattern comprehension
Xinnan Dai, Haohao Qu, Yifen Shen, Bohang Zhang, Qihao Wen, Wenqi Fan, Dongsheng Li, Jiliang Tang, and Caihua Shan · 2024
Later among the works it cites.
Are large-language models graph algorithmic reasoners?
Alexander K Taylor, Anthony Cuturrufo, Vishal Yathish, Mingyu Derek Ma, and Wei Wang · 2024
Later among the works it cites.
Investigating instruction tuning large language models on graphs
Kerui Zhu, Bo-Wei Huang, Bowen Jin, Yizhu Jiao, Ming Zhong, Kevin Chang, Shou-De Lin, and Jiawei Han · 2024
Later among the works it cites.
Can llm graph reasoning generalize beyond pattern memorization?
Yizhuo Zhang, Heng Wang, Shangbin Feng, Zhaoxuan Tan, Xiaochuang Han, Tianxing He, and Yulia Tsvetkov · 2024
Later among the works it cites.
Treetop: Topology-aware fine-tuning for llm conversation tree understanding
Jashn Arora, Rahul Madhavan, Karthikeyan Shanmugam, John Palowitch, and Manish Jain · 2024
Later among the works it cites.
Hetgpt: Harnessing the power of prompt tuning in pre-trained heterogeneous graph neural networks
Yihong Ma, Ning Yan, Jiayu Li, Masood Mortazavi, and Nitesh V Chawla · 2024
Later among the works it cites.
Bootstrapping heterogeneous graph representation learning via large language models: A generalized approach
Hang Gao, Chenhao Zhang, Fengge Wu, Junsuo Zhao, Changwen Zheng, and Huaping Liu · 2024
Later among the works it cites.
Higpt: Heterogeneous graph language model
Jiabin Tang, Yuhao Yang, Wei Wei, Lei Shi, Long Xia, Dawei Yin, and Chao Huang · 2024
Later among the works it cites.
Litfm: A retrieval augmented structure-aware foundation model for citation graphs
Jiasheng Zhang, Jialin Chen, Ali Maatouk, Ngoc Bui, Qianqian Xie, Leandros Tassiulas, Jie Shao, Hua Xu, and Rex Ying · 2024
Later among the works it cites.
Mint: Multi-network training for transfer learning on temporal graphs
Kiarash Shamsi, Tran Gia Bao Ngo, Razieh Shirzadkhani, Shenyang Huang, Farimah Poursafaei, Poupak Azad, Reihaneh Rabbany, Baris Coskunuzer, Guillaume Rabusseau, and Cuneyt Gurcan Akcora · 2024
Later among the works it cites.
Llm4dyg: can large language models solve spatial-temporal problems on dynamic graphs?
Zeyang Zhang, Xin Wang, Ziwei Zhang, Haoyang Li, Yijian Qin, and Wenwu Zhu · 2024
Later among the works it cites.
Flowgpt: How long can llms trace back and predict the trends of graph dynamics?
Zijian Zhang, Zonghan Zhang, and Zhiqian Chen · 2024
Later among the works it cites.
Clear: Can language models really understand causal graphs?
Sirui Chen, Mengying Xu, Kun Wang, Xingyu Zeng, Rui Zhao, Shengjie Zhao, and Chaochao Lu · 2024
Later among the works it cites.
Towards neural scaling laws on graphs
Jingzhe Liu, Haitao Mao, Zhikai Chen, Tong Zhao, Neil Shah, and Jiliang Tang · 2024
Later among the works it cites.
Do neural scaling laws exist on graph self-supervised learning?
Qian Ma, Haitao Mao, Jingzhe Liu, Zhehua Zhang, Chunlin Feng, Yu Song, Yihan Shao, and Yao Ma · 2024
Later among the works it cites.
Text-space graph foundation models: Comprehensive benchmarks and new insights
Zhikai Chen, Haitao Mao, Jingzhe Liu, Yu Song, Bingheng Li, Wei Jin, Bahare Fatemi, Anton Tsitsulin, Bryan Perozzi, Hui Liu, et al · 2024
Later among the works it cites.
Dtgb: A comprehensive benchmark for dynamic text-attributed graphs
Jiasheng Zhang, Jialin Chen, Menglin Yang, Aosong Feng, Shuang Liang, Jie Shao, and Rex Ying · 2024
Later among the works it cites.
Taglas: An atlas of text-attributed graph datasets in the era of large graph and language models
Jiarui Feng, Hao Liu, Lecheng Kong, Yixin Chen, and Muhan Zhang · 2024
Later among the works it cites.
Toxcast: [dataset details for toxcast], 2024
Author et al · 2024
Later among the works it cites.
Graphfm: A comprehensive benchmark for graph foundation model
Yuhao Xu, Xinqi Liu, Keyu Duan, Yi Fang, Yu-Neng Chuang, Daochen Zha, and Qiaoyu Tan · 2024
Later among the works it cites.
Position: Future directions in the theory of graph machine learning
Christopher Morris, Fabrizio Frasca, Nadav Dym, Haggai Maron, Ismail Ilkan Ceylan, Ron Levie, Derek Lim, Michael M. Bronstein, Martin Grohe, and Stefanie Jegelka · 2024
Later among the works it cites.
Crawl4ai
Crawl4ai team · 2024
Later among the works it cites.
On llms-driven synthetic data generation, curation, and evaluation: A survey
Lin Long, Rui Wang, Ruixuan Xiao, Junbo Zhao, Xiao Ding, Gang Chen, and Haobo Wang · 2024
Later among the works it cites.
Rho-1: Not all tokens are what you need
Zhenghao Lin, Zhibin Gou, Yeyun Gong, Xiao Liu, Yelong Shen, Ruochen Xu, Chen Lin, Yujiu Yang, Jian Jiao, Nan Duan, et al · 2024
Later among the works it cites.
Less: Selecting influential data for targeted instruction tuning
Mengzhou Xia, Sadhika Malladi, Suchin Gururangan, Sanjeev Arora, and Danqi Chen · 2024
Later among the works it cites.
Towards a general gnn framework for combinatorial optimization
Frederik Wenkel, Semih Cantürk, Michael Perlmutter, and Guy Wolf · 2024
Later among the works it cites.
Position: Relational deep learning-graph representation learning on relational databases
Matthias Fey, Weihua Hu, Kexin Huang, Jan Eric Lenssen, Rishabh Ranjan, Joshua Robinson, Rex Ying, Jiaxuan You, and Jure Leskovec · 2024
Later among the works it cites.
AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection
Hezhe Qiao, Chaoxi Niu, Ling Chen, and Guansong Pang · 2025
Closest in time.
Graph foundation models: Concepts, opportunities and challenges
Jiawei Liu, Cheng Yang, Zhiyuan Lu, Junze Chen, Yibo Li, Mengmei Zhang, Ting Bai, Yuan Fang, Lichao Sun, Philip S Yu, et al · 2025
Closest in time.
Towards graph foundation models: A transferability perspective
Yuxiang Wang, Wenqi Fan, Suhang Wang, and Yao Ma · 2025
Closest in time.
A survey of cross-domain graph learning: Progress and future directions
Haihong Zhao, Chenyi Zi, Aochuan Chen, and Jia Li · 2025
Closest in time.
Graph foundation models for recommendation: A comprehensive survey
Bin Wu, Yihang Wang, Yuanhao Zeng, Jiawei Liu, Jiashu Zhao, Cheng Yang, Yawen Li, Long Xia, Dawei Yin, and Chuan Shi · 2025
Closest in time.
GOFA: A generative one-for-all model for joint graph language modeling
Lecheng Kong, Jiarui Feng, Hao Liu, Chengsong Huang, Jiaxin Huang, Yixin Chen, and Muhan Zhang · 2025
Closest in time.
Samgpt: Text-free graph foundation model for multi-domain pre-training and cross-domain adaptation
Xingtong Yu, Zechuan Gong, Chang Zhou, Yuan Fang, and Hui Zhang · 2025
Closest in time.
Fully-inductive node classification on arbitrary graphs
Jianan Zhao, Zhaocheng Zhu, Mikhail Galkin, Hesham Mostafa, Michael M Bronstein, and Jian Tang · 2025
Closest in time.
Edge prompt tuning for graph neural networks
Xingbo Fu, Yinhan He, and Jundong Li · 2025
Closest in time.
Holographic node representations: Pre-training task-agnostic node embeddings
Beatrice Bevilacqua, Joshua Robinson, Jure Leskovec, and Bruno Ribeiro · 2025
Closest in time.
Neural graph pattern machine
Zehong Wang, Zheyuan Zhang, Tianyi Ma, Nitesh V Chawla, Chuxu Zhang, and Yanfang Ye · 2025
Closest in time.
Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al · 2025
Closest in time.
Training mlps on graphs without supervision
Zehong Wang, Zheyuan Zhang, Chuxu Zhang, and Yanfang Ye · 2025
Closest in time.
Learning accurate, efficient, and interpretable mlps on multiplex graphs via node-wise multi-view ensemble distillation
Yunhui Liu, Zhen Tao, Xiang Zhao, Jianhua Zhao, Tao Zheng, and Tieke He · 2025
Closest in time.
A comprehensive survey on test-time adaptation under distribution shifts
Jian Liang, Ran He, and Tieniu Tan · 2025
Closest in time.
Instance-aware graph prompt learning
Jiazheng Li, Jundong Li, and Chuxu Zhang · 2025
Closest in time.
Fairness-aware prompt tuning for graph neural networks
Zhengpin Li, Minhua Lin, Jian Wang, and Suhang Wang · 2025
Closest in time.
Non-homophilic graph pre-training and prompt learning
Xingtong Yu, Jie Zhang, Yuan Fang, and Renhe Jiang · 2025
Closest in time.
Are large language models in-context graph learners?
Jintang Li, Ruofan Wu, Yuchang Zhu, Huizhe Zhang, Liang Chen, and Zibin Zheng · 2025
Closest in time.
Riemanngfm: Learning a graph foundation model from riemannian geometry
Li Sun, Zhenhao Huang, Suyang Zhou, Qiqi Wan, Hao Peng, and Philip Yu · 2025
Closest in time.
Unigraph2: Learning a unified embedding space to bind multimodal graphs
Yufei He, Yuan Sui, Xiaoxin He, Yue Liu, Yifei Sun, and Bryan Hooi · 2025
Closest in time.
Handling feature heterogeneity with learnable graph patches
Yifei Sun, Yang Yang, Xiao Feng, Zijun Wang, Haoyang Zhong, Chunping Wang, and Lei Chen · 2025
Closest in time.
Can llms convert graphs to text-attributed graphs?
Zehong Wang, Sidney Liu, Zheyuan Zhang, Tianyi Ma, Chuxu Zhang, and Yanfang Ye · 2025
Closest in time.
Graphicl: Unlocking graph learning potential in llms through structured prompt design
Yuanfu Sun, Zhengnan Ma, Yi Fang, Jing Ma, and Qiaoyu Tan · 2025
Closest in time.
Multi-domain graph foundation models: Robust knowledge transfer via topology alignment
Shuo Wang, Bokui Wang, Zhixiang Shen, Boyan Deng, and Zhao Kang · 2025
Closest in time.
Each graph is a new language: Graph learning with llms
Huachi Zhou, Jiahe Du, Chuang Zhou, Chang Yang, Yilin Xiao, Yuxuan Xie, and Xiao Huang · 2025
Closest in time.
Adaptive expansion for hypergraph learning
Tianyi Ma, Yiyue Qian, Shinan Zhang, Chuxu Zhang, and Yanfang Ye · 2025
Closest in time.
Llm-empowered class imbalanced graph prompt learning for online drug trafficking detection
Tianyi Ma, Yiyue Qian, Zehong Wang, Zheyuan Zhang, Chuxu Zhang, and Yanfang Ye · 2025
Closest in time.
A comprehensive analysis on llm-based node classification algorithms
Xixi Wu, Yifei Shen, Fangzhou Ge, Caihua Shan, Yizhu Jiao, Xiangguo Sun, and Hong Cheng · 2025
Closest in time.
Adaptive graph enhancement for imbalanced multi-relation graph learning
Yiyue Qian, Tianyi Ma, Chuxu Zhang, and Yanfang Ye · 2025
Closest in time.
Explaining the explainers in graph neural networks: a comparative study
Antonio Longa, Steve Azzolin, Gabriele Santin, Giulia Cencetti, Pietro Liò, Bruno Lepri, and Andrea Passerini · 2025
Closest in time.
How expressive are knowledge graph foundation models?
Xingyue Huang, Pablo Barceló, Michael M Bronstein, İsmail İlkan Ceylan, Mikhail Galkin, Juan L Reutter, and Miguel Romero Orth · 2025
Closest in time.
A prompt-based knowledge graph foundation model for universal in-context reasoning
Yuanning Cui, Zequn Sun, and Wei Hu · 2025
Closest in time.
GT2Vec: Large Language Models as Multi-Modal Encoders for Text and Graph-Structured Data
Jiacheng Lin, Kun Qian, Haoyu Han, Nurendra Choudhary, Tianxin Wei, Zhongruo Wang, Sahika Genc, Edward W. Huang, Sheng Wang, Karthik Subbian, Danai Koutra, and Jimeng Sun · 2025
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Gfm-rag: Graph foundation model for retrieval augmented generation
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