Xing Fu

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All published works

Action	Title	Year	Authors
+ PDF Chat	Self-supervision meets kernel graph neural models: From architecture to augmentations	2023	Jiawang Dan Ruofan Wu Yunpeng Liu Baokun Wang Changhua Meng Tengfei Liu Tianyi Zhang Ningtao Wang Xing Fu Qi Li
+	Self-supervision meets kernel graph neural models: From architecture to augmentations	2023	Jiawang Dan Ruofan Wu Yunpeng Liu Baokun Wang Changhua Meng Tengfei Liu Tianyi Zhang Ningtao Wang Xing Fu Qi Li

Common Coauthors

Coauthor	Papers Together
Ruofan Wu	2
Tengfei Liu	2
Ningtao Wang	2
Changhua Meng	2
Yunpeng Liu	2
Baokun Wang	2
Jiawang Dan	2
Tianyi Zhang	2
Weiqiang Wang	1
Qi Li	1
Qi Li	1
Weiqiang Wang	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+ PDF Chat	A Survey of Statistical Network Models	2009	Anna Goldenberg	1
+ PDF Chat	Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs	2017	Martin Simonovsky Nikos Komodakis	1
+	Relational inductive biases, deep learning, and graph networks	2018	Peter Battaglia Jessica B. Hamrick Victor Bapst Álvaro Sánchez‐González Vinícius Zambaldi Mateusz Malinowski Andrea Tacchetti David Raposo Adam Santoro Ryan Faulkner	1
+	Proceedings of the 25th international conference on Machine learning	2008	William W. Cohen Andrew McCallum Sam T. Roweis	1
+	Hierarchical Graph Representation Learning with Differentiable Pooling	2018	Rex Ying Jiaxuan You Christopher Morris Xiang Ren William L. Hamilton Jure Leskovec	1
+	How Powerful are Graph Neural Networks?	2018	Keyulu Xu Weihua Hu Jure Leskovec Stefanie Jegelka	1
+	Semi-Supervised Classification with Graph Convolutional Networks	2016	Thomas Kipf Max Welling	1
+	Graph Contrastive Learning with Augmentations	2020	Yuning You Tianlong Chen Yongduo Sui Ting Chen Zhangyang Wang Yang Shen	1
+ PDF Chat	GCC	2020	Jiezhong Qiu Qibin Chen Yuxiao Dong Jing Zhang Hongxia Yang Ming Ding Kuansan Wang Jie Tang	1
+	Adversarial Graph Augmentation to Improve Graph Contrastive Learning	2021	Susheel Suresh Li Pan Cong Hao Jennifer Neville	1
+ PDF Chat	Exploring Simple Siamese Representation Learning	2021	Xinlei Chen Kaiming He	1
+ PDF Chat	Self-Supervised Learning on Graphs: Contrastive, Generative, or Predictive	2021	Lirong Wu Haitao Lin Cheng Tan Zhangyang Gao Stan Z. Li	1
+ PDF Chat	KerGNNs: Interpretable Graph Neural Networks with Graph Kernels	2022	Aosong Feng Chenyu You Shiqiang Wang Leandros Tassiulas	1
+	Weisfeiler and Leman go Machine Learning: The Story so far	2021	Christopher G. Morris Yaron Lipman Haggai Maron Bastian Rieck Nils M. Kriege Martin Grohe Matthias Fey Karsten Borgwardt	1
+	A Fair Comparison of Graph Neural Networks for Graph Classification	2019	Federico Errica Marco Podda Davide Bacciu Alessio Micheli	1
+	Diffusion Improves Graph Learning	2019	Johannes Gasteiger Stefan Weißenberger Stephan Günnemann	1
+	InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization	2019	Fan-Yun Sun Jordan Hoffmann Vikas Verma Jian Tang	1
+	GraphMAE: Self-Supervised Masked Graph Autoencoders	2022	Zhenyu Hou Xiao Liu Yukuo Cen Yuxiao Dong Hongxia Yang Chunjie Wang Jie Tang	1
+	Inductive Representation Learning on Large Graphs	2017	William L. Hamilton Rex Ying Jure Leskovec	1
+	PyTorch: An Imperative Style, High-Performance Deep Learning Library	2019	Adam Paszke Sam Gross Francisco Massa Adam Lerer James T. Bradbury Gregory Chanan Trevor Killeen Zeming Lin Natalia Gimelshein Luca Antiga	1
+	Representation Learning with Contrastive Predictive Coding	2018	Aäron van den Oord Yazhe Li Oriol Vinyals	1
+	Spectral Augmentation for Self-Supervised Learning on Graphs	2022	Lu Lin Jinghui Chen Hongning Wang	1
+	Variational Graph Auto-Encoders	2016	Thomas Kipf Max Welling	1