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Graph Neural Networks (GNNs) have demonstrated remarkable proficiency in handling a range of graph analytical tasks across various domains, such as e-commerce and social networks.
The pagerank citation ranking: Bring order to the web
Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. 1998 · 1998
Earlier work this paper cites.
Birds of a Feather: Homophily in Social Networks
Miller McPherson, Lynn Smith-Lovin, and James M Cook. 2001 · 2001
Earlier work this paper cites.
Local Graph Partitioning using PageRank Vectors. In FOCS . 475–486
Reid Andersen, Fan R. K. Chung, and Kevin J. Lang. 2006 · 2006
Earlier work this paper cites.
Integrating structured biological data by Kernel Maximum Mean Discrepancy. In ISMB (Supplement of Bioinformatics) . 49–57
Karsten M. Borgwardt, Arthur Gretton, Malte J. Rasch, Hans-Peter Kriegel, Bernhard Schölkopf, and Alexander J. Smola. 2006 · 2006
Earlier work this paper cites.
Visualizing data using t-SNE
Laurens Van der Maaten and Geoffrey Hinton. 2008 · 2008
Earlier work this paper cites.
Motif-based Classification in Journal Citation Networks
Wenchen Wu, Yanni Han, and Deyi Li. 2008 · 2008
Earlier work this paper cites.
A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan. 2010 · 2010
Earlier work this paper cites.
Motif Analysis in the Amazon Product Co-Purchasing Network
Abhishek Srivastava. 2010 · 2010
Earlier work this paper cites.
A Kernel Two-Sample Test
Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, and Alexander J. Smola. 2012 · 2012
Earlier work this paper cites.
Glove: Global Vectors for Word Representation. In EMNLP . 1532–1543
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization. In ICLR (Poster)
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Earlier work this paper cites.
Image-Based Recommendations on Styles and Substitutes. In SIGIR . 43–52
Julian J. McAuley, Christopher Targett, Qinfeng Shi, and Anton van den Hengel. 2015 · 2015
Earlier work this paper cites.
Revisiting Semi-Supervised Learning with Graph Embeddings. In ICML . 40–48
Zhilin Yang, William W. Cohen, and Ruslan Salakhutdinov. 2016 · 2016
Earlier work this paper cites.
Neural Message Passing for Quantum Chemistry. In ICML . 1263–1272
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl. 2017 · 2017
Earlier work this paper cites.
Inductive Representation Learning on Large Graphs. In NeurIPS . 1024–1034
William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Earlier work this paper cites.
Semi-Supervised Classification with Graph Convolutional Networks. In ICLR (Poster)
Thomas N. Kipf and Max Welling. 2017 · 2017
Earlier work this paper cites.
Co-Regularized Deep Multi-Network Embedding. In WWW . 469–478
Jingchao Ni, Shiyu Chang, Xiao Liu, Wei Cheng, Haifeng Chen, Dongkuan Xu, and Xiang Zhang. 2018 · 2018
Earlier work this paper cites.
Pitfalls of Graph Neural Network Evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Günnemann. 2018 · 2018
Earlier work this paper cites.
Representation Learning with Contrastive Predictive Coding
Aäron van den Oord, Yazhe Li, and Oriol Vinyals. 2018 · 2018
Earlier work this paper cites.
Graph Attention Networks. In ICLR (Poster)
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018 · 2018
Earlier work this paper cites.
Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba. In KDD . 839–848
Jizhe Wang, Pipei Huang, Huan Zhao, Zhibo Zhang, Binqiang Zhao, and Dik Lun Lee. 2018 · 2018
Earlier work this paper cites.
TopPPR: Top-k Personalized PageRank Queries with Precision Guarantees on Large Graphs. In SIGMOD Conference . 441–456
Zhewei Wei, Xiaodong He, Xiaokui Xiao, Sibo Wang, Shuo Shang, and Ji-Rong Wen. 2018 · 2018
Earlier work this paper cites.
Parameter-Efficient Transfer Learning for NLP. In ICML . 2790–2799
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin de Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019 · 2019
Cited alongside, same era.
Diffusion Improves Graph Learning. In NeurIPS . 13333–13345
Johannes Klicpera, Stefan Weißenberger, and Stephan Günnemann. 2019 · 2019
Cited alongside, same era.
Contrastive Multi-View Representation Learning on Graphs. In ICML . 4116–4126
Kaveh Hassani and Amir Hosein Khas Ahmadi. 2020 · 2020
Cited alongside, same era.
Open Graph Benchmark: Datasets for Machine Learning on Graphs. In NeurIPS
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. 2020 · 2020
Cited alongside, same era.
Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs. In NeurIPS . 10294–10305
Dasol Hwang, Jinyoung Park, Sunyoung Kwon, Kyung-Min Kim, Jung-Woo Ha, and Hyunwoo J. Kim. 2020 · 2020
Cited alongside, same era.
LoRA: Low-Rank Adaptation of Large Language Models. In ICLR
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022 · 2022
Later among the works it cites.
Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Models. In NeurIPS
Fan Liu, Hao Liu, and Wenzhao Jiang. 2022 · 2022
Later among the works it cites.
GPPT: Graph Pre-training and Prompt Tuning to Generalize Graph Neural Networks. In KDD . 1717–1727
Mingchen Sun, Kaixiong Zhou, Xin He, Ying Wang, and Xin Wang. 2022 · 2022
Later among the works it cites.
A Bi-directional Recommender System for Online Recruitment. In ICDM . 628–637
Zhe-Rui Yang, Zhen-Yu He, Chang-Dong Wang, Pei-Yuan Lai, De-Zhang Liao, and Zhong-Zheng Wang. 2022 · 2022
Later among the works it cites.
BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models. In ACL (2) . 1–9
Elad Ben Zaken, Yoav Goldberg, and Shauli Ravfogel. 2022 · 2022
Later among the works it cites.
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Exploring Versatile Generative Language Model Via Parameter-Efficient Transfer Learning. In EMNLP (Findings) . 441–459
Zhaojiang Lin, Andrea Madotto, and Pascale Fung. 2020 · 2020
Cited alongside, same era.
Geom-GCN: Geometric Graph Convolutional Networks. In ICLR
Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang. 2020 · 2020
Cited alongside, same era.
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training. In KDD . 1150–1160
Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, and Jie Tang. 2020 · 2020
Cited alongside, same era.
Hyper-Parameter Optimization: A Review of Algorithms and Applications
Tong Yu and Hong Zhu. 2020 · 2020
Cited alongside, same era.
Revisiting Graph Neural Networks for Link Prediction
Muhan Zhang, Pan Li, Yinglong Xia, Kai Wang, and Long Jin. 2020 · 2020
Cited alongside, same era.
Multi-Channel Graph Neural Networks. In IJCAI . 1352–1358
Kaixiong Zhou, Qingquan Song, Xiao Huang, Daochen Zha, Na Zou, and Xia Hu. 2020 · 2020
Cited alongside, same era.
Deep Graph Contrastive Representation Learning
Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. 2020 · 2020
Cited alongside, same era.
TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation. In RecSys . 1007–1014
Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023 · 2023
Later among the works it cites.
When to Pre-Train Graph Neural Networks? From Data Generation Perspective!. In KDD . 142–153
Yuxuan Cao, Jiarong Xu, Carl J. Yang, Jiaan Wang, Yunchao Zhang, Chunping Wang, Lei Chen, and Yang Yang. 2023 · 2023
Later among the works it cites.
Graph Transfer Learning via Adversarial Domain Adaptation With Graph Convolution
Quanyu Dai, Xiao-Ming Wu, Jiaren Xiao, Xiao Shen, and Dan Wang. 2023 · 2023
Later among the works it cites.
Universal Prompt Tuning for Graph Neural Networks. In NeurIPS
Taoran Fang, Yunchao Zhang, Yang Yang, Chunping Wang, and Lei Chen. 2023 · 2023
Later among the works it cites.
Adapt in Contexts: Retrieval-Augmented Domain Adaptation via In-Context Learning. In EMNLP . 6525–6542
Quanyu Long, Wenya Wang, and Sinno Jialin Pan. 2023 · 2023
Later among the works it cites.
All in One: Multi-Task Prompting for Graph Neural Networks. In KDD . 2120–2131
Xiangguo Sun, Hong Cheng, Jia Li, Bo Liu, and Jihong Guan. 2023 · 2023
Later among the works it cites.
Lingling Xu, Haoran Xie, Si-Zhao Joe Qin, Xiaohui Tao, and Fu Lee Wang. 2023 · 2023
Later among the works it cites.
The Expressive Power of Low-Rank Adaptation
Yuchen Zeng and Kangwook Lee. 2023 · 2023
Later among the works it cites.
Adding Conditional Control to Text-to-Image Diffusion Models. In ICCV . IEEE, 3813–3824
Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. 2023 · 2023
Later among the works it cites.
Yun Zhu, Yaoke Wang, Haizhou Shi, Zhenshuo Zhang, and Siliang Tang. 2023 · 2023
Later among the works it cites.
G-Adapter: Towards Structure-Aware Parameter-Efficient Transfer Learning for Graph Transformer Networks. In AAAI . 12226–12234
Anchun Gui, Jinqiang Ye, and Han Xiao. 2024 · 2024
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Investigating Out-of-Distribution Generalization of GNNs: An Architecture Perspective
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Distribution Consistency based Self-Training for Graph Neural Networks with Sparse Labels
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