Fetching the paper…
Reading the bibliography…
While Graph Neural Networks (GNNs) are remarkably successful in a variety of high-impact applications, we demonstrate that, in link prediction, the common practices of including the edges being predicted in the graph at training and/or test have outsized impact on the performance of low-degree nodes.
On power-law relationships of the internet topology
Michalis Faloutsos, Petros Faloutsos, and Christos Faloutsos. 1999 · 1999
Earlier work this paper cites.
Friends and neighbors on the web
Lada A Adamic and Eytan Adar. 2003 · 2003
Earlier work this paper cites.
The link prediction problem for social networks. In Proceedings of the twelfth international conference on Information and knowledge management . 556–559
David Liben-Nowell and Jon Kleinberg. 2003 · 2003
Earlier work this paper cites.
Graphs over time: densification laws, shrinking diameters and possible explanations. In Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining . 177–187
Jure Leskovec, Jon Kleinberg, and Christos Faloutsos. 2005 · 2005
Earlier work this paper cites.
Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky. 2009 · 2009
Earlier work this paper cites.
Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013 · 2013
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2016a · 2016
Earlier work this paper cites.
Variational graph auto-encoders
Thomas N Kipf and Max Welling. 2016b · 2016
Earlier work this paper cites.
A survey of link prediction in complex networks
Víctor Martínez, Fernando Berzal, and Juan-Carlos Cubero. 2016 · 2016
Earlier work this paper cites.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Earlier work this paper cites.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017 · 2017
Earlier work this paper cites.
struc2vec: Learning node representations from structural identity. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining . 385–394
Leonardo FR Ribeiro, Pedro HP Saverese, and Daniel R Figueiredo. 2017 · 2017
Earlier work this paper cites.
Hyperspherical variational auto-encoders
Tim R Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, and Jakub M Tomczak. 2018 · 2018
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Earlier work this paper cites.
Representation learning on graphs with jumping knowledge networks. In International conference on machine learning . PMLR, 5453–5462
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka. 2018 · 2018
Earlier work this paper cites.
Link prediction based on graph neural networks
Muhan Zhang and Yixin Chen. 2018 · 2018
Cited alongside, same era.
Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds
Matthias Fey and Jan E. Lenssen. 2019 · 2019
Cited alongside, same era.
Strategies for pre-training graph neural networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec. 2019 · 2019
Cited alongside, same era.
Composition-based multi-relational graph convolutional networks
Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, and Partha Talukdar. 2019 · 2019
Cited alongside, same era.
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, Tianjun Xiao, Tong He, George Karypis, Jinyang Li, and Zheng Zhang. 2019 · 2019
How attentive are graph attention networks?
Shaked Brody, Uri Alon, and Eran Yahav. 2021 · 2021
Later among the works it cites.
Link prediction with persistent homology: An interactive view. In International Conference on Machine Learning . PMLR, 11659–11669
Zuoyu Yan, Tengfei Ma, Liangcai Gao, Zhi Tang, and Chao Chen. 2021 · 2021
Later among the works it cites.
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 · 2021
Later among the works it cites.
Neural bellman-ford networks: A general graph neural network framework for link prediction
Zhaocheng Zhu, Zuobai Zhang, Louis-Pascal Xhonneux, and Jian Tang. 2021 · 2021
Later among the works it cites.
FakeEdge: Alleviate Dataset Shift in Link Prediction
Kaiwen Dong, Yijun Tian, Zhichun Guo, Yang Yang, and Nitesh V Chawla. 2022 · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Position-aware graph neural networks. In International conference on machine learning . PMLR, 7134–7143
Jiaxuan You, Rex Ying, and Jure Leskovec. 2019 · 2019
Cited alongside, same era.
Inductive matrix completion based on graph neural networks
Muhan Zhang and Yixin Chen. 2019 · 2019
Cited alongside, same era.
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. 2020b · 2020
Cited alongside, same era.
Mining of massive data sets
Jure Leskovec, Anand Rajaraman, and Jeffrey David Ullman. 2020 · 2020
Cited alongside, same era.
Commonsense knowledge base completion with structural and semantic context. In Proceedings of the AAAI conference on artificial intelligence , Vol. 34. 2925–2933
Chaitanya Malaviya, Chandra Bhagavatula, Antoine Bosselut, and Yejin Choi. 2020 · 2020
Cited alongside, same era.
Investigating and mitigating degree-related biases in graph convoltuional networks. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management . 1435–1444
Xianfeng Tang, Huaxiu Yao, Yiwei Sun, Yiqi Wang, Jiliang Tang, Charu Aggarwal, Prasenjit Mitra, and Suhang Wang. 2020 · 2020
Cited alongside, same era.
Inductive relation prediction by subgraph reasoning. In International Conference on Machine Learning . PMLR, 9448–9457
Komal Teru, Etienne Denis, and Will Hamilton. 2020 · 2020
Cited alongside, same era.
Later among the works it cites.
Vijay Prakash Dwivedi, Ladislav Rampášek, Mikhail Galkin, Ali Parviz, Guy Wolf, Anh Tuan Luu, and Dominique Beaini. 2022 · 2022
Later among the works it cites.
Efficient and effective training of language and graph neural network models
Vassilis N Ioannidis, Xiang Song, Da Zheng, Houyu Zhang, Jun Ma, Yi Xu, Belinda Zeng, Trishul Chilimbi, and George Karypis. 2022 · 2022
Later among the works it cites.
Shopping Queries Dataset: A Large-Scale ESCI Benchmark for Improving Product Search
Chandan K. Reddy, Lluís Màrquez, Fran Valero, Nikhil Rao, Hugo Zaragoza, Sambaran Bandyopadhyay, Arnab Biswas, Anlu Xing, and Karthik Subbian. 2022 · 2022
Later among the works it cites.
Time travel in llms: Tracing data contamination in large language models
Shahriar Golchin and Mihai Surdeanu. 2023 · 2023
Closest in time.
NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark
Oscar Sainz, Jon Ander Campos, Iker García-Ferrero, Julen Etxaniz, Oier Lopez de Lacalle, and Eneko Agirre. 2023 · 2023
Closest in time.
Neural Common Neighbor with Completion for Link Prediction
Xiyuan Wang, Haotong Yang, and Muhan Zhang. 2023 · 2023
Closest in time.
Don’t Make Your LLM an Evaluation Benchmark Cheater
Kun Zhou, Yutao Zhu, Zhipeng Chen, Wentong Chen, Wayne Xin Zhao, Xu Chen, Yankai Lin, Ji-Rong Wen, and Jiawei Han. 2023b · 2023
Closest in time.
Explore Spurious Correlations at the Concept Level in Language Models for Text Classification
Yuhang Zhou, Paiheng Xu, Xiaoyu Liu, Bang An, Wei Ai, and Furong Huang. 2023a · 2023
Closest in time.
TouchUp-G: Improving Feature Representation through Graph-Centric Finetuning
Jing Zhu, Xiang Song, Vassilis N Ioannidis, Danai Koutra, and Christos Faloutsos. 2023 · 2023
Closest in time.