Fetching the paper…
Reading the bibliography…
Despite a surge in interest in GNN development, homogeneity in benchmarking datasets still presents a fundamental issue to GNN research.
Simplifying Graph Convolutional Networks
Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr. au2, Christopher Fifty, Tao Yu, and Kilian Q. Weinberger. 2019 · 1902
Earlier work this paper cites.
GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation
Marc Brockschmidt. 2020 · 1906
Earlier work this paper cites.
The Anatomy of a Large-Scale Hypertextual Web Search Engine
Sergey Brin and Lawrence Page. 1998 · 1998
Earlier work this paper cites.
Small Worlds: the dynamics of networks between order and randomness
D.J. Watts. 1999 · 1999
Earlier work this paper cites.
Benchmark graphs for testing community detection algorithms
Andrea Lancichinetti, Santo Fortunato, and Filippo Radicchi. 2008 · 2008
Earlier work this paper cites.
Power-Law Distributions in Empirical Data
Aaron Clauset, Cosma Rohilla Shalizi, and M. E. J. Newman. 2009 · 2009
Earlier work this paper cites.
Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification
Yunsheng Shi, Zhengjie Huang, Shikun Feng, Hui Zhong, Wenjin Wang, and Yu Sun. 2021 · 2009
Earlier work this paper cites.
Stochastic blockmodels and community structure in networks
Brian Karrer and M. E. J. Newman. 2011 · 2011
Earlier work this paper cites.
Network science
Albert-László Barabási and Márton Pósfai. 2016 · 2016
Cited alongside, same era.
Semi-Supervised Classification with Graph Convolutional Networks
Thomas N. Kipf and Max Welling. 2017 · 2017
Cited alongside, same era.
Inductive Representation Learning on Large Graphs
William L. Hamilton, Rex Ying, and Jure Leskovec. 2018 · 2018
Cited alongside, same era.
Pitfalls of graph neural network evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Günnemann. 2018 · 2018
Cited alongside, same era.
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018 · 2018
On the Impact of Communities on Semi-supervised Classification Using Graph Neural Networks
Hussain Hussain, Tomislav Duricic, Elisabeth Lex, Roman Kern, and Denis Helic. 2021 · 2021
Later among the works it cites.
Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining . ACM
Susheel Suresh, Vinith Budde, Jennifer Neville, Pan Li, and Jianzhu Ma. 2021 · 2021
Later among the works it cites.
How Attentive are Graph Attention Networks?
Shaked Brody, Uri Alon, and Eran Yahav. 2022 · 2022
Later among the works it cites.
Predict then Propagate: Graph Neural Networks meet Personalized PageRank
Johannes Gasteiger, Aleksandar Bojchevski, and Stephan Günnemann. 2022 · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
How Powerful are Graph Neural Networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019 · 2019
Cited alongside, same era.
Scale-Free, Attributed and Class-Assortative Graph Generation to Facilitate Introspection of Graph Neural Networks. In KDD Mining and Learning with Graphs
Neil Shah. 2020 · 2020
Cited alongside, same era.
Graph Neural Networks with Convolutional ARMA Filters
Filippo Maria Bianchi, Daniele Grattarola, Lorenzo Livi, and Cesare Alippi. 2021 · 2021
Cited alongside, same era.
Abdalsamad Keramatfar, Mohadeseh Rafiee, and Hossein Amirkhani. 2022 · 2022
Later among the works it cites.
How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision
Dongkwan Kim and Alice Oh. 2022 · 2022
Later among the works it cites.
GraphWorld. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . ACM
John Palowitch, Anton Tsitsulin, Brandon Mayer, and Bryan Perozzi. 2022 · 2022
Later among the works it cites.