Revisiting Semi-Supervised Learning with Graph Embeddings

29 March 2016

Papers citing "Revisiting Semi-Supervised Learning with Graph Embeddings"

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Title
GraphPatcher: Mitigating Degree Bias for Graph Neural Networks via Test-time Augmentation Mingxuan Ju Tong Zhao Wenhao Yu Neil Shah Yanfang Ye 36 13 0 01 Oct 2023
Learning How to Propagate Messages in Graph Neural Networks Teng Xiao Zhengyu Chen Donglin Wang Suhang Wang GNN 34 76 0 01 Oct 2023
One for All: Towards Training One Graph Model for All Classification Tasks Hao Liu Jiarui Feng Lecheng Kong Ningyue Liang Dacheng Tao Yixin Chen Muhan Zhang AI4CE 15 113 0 29 Sep 2023
Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why? Jin Huang Xingjian Zhang Qiaozhu Mei Jiaqi Ma 40 14 0 28 Sep 2023
Distill to Delete: Unlearning in Graph Networks with Knowledge Distillation Yash Sinha Murari Mandal Mohan S. Kankanhalli MU 39 11 0 28 Sep 2023
Provable Training for Graph Contrastive Learning Yue Yu Tianlin Li Mengmei Zhang Nian Liu Chuan Shi 29 9 0 25 Sep 2023
From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited Zheng Wang H. Ding L. Pan Jianhua Li Zhiguo Gong Philip S. Yu GNN 33 5 0 24 Sep 2023
Higher-order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial Complexes Yiming Huang Yujie Zeng Qiang Wu Linyuan Lu 27 18 0 22 Sep 2023
InkStream: Real-time GNN Inference on Streaming Graphs via Incremental Update Dan Wu Zhaoying Li Tulika Mitra GNN 14 2 0 20 Sep 2023
Graph Contrastive Learning Meets Graph Meta Learning: A Unified Method for Few-shot Node Tasks Hao Liu Jiarui Feng Lecheng Kong Dacheng Tao Yixin Chen Muhan Zhang OffRL 37 5 0 19 Sep 2023
FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networks Qiying Pan Ruofan Wu Tengfei Liu Tianyi Zhang Yifei Zhu Weiqiang Wang FedML 48 9 0 18 Sep 2023
Mitigating Over-Smoothing and Over-Squashing using Augmentations of Forman-Ricci Curvature Lukas Fesser Melanie Weber 65 21 0 17 Sep 2023
Bregman Graph Neural Network Jiayu Zhai Lequan Lin Dai Shi Junbin Gao 21 4 0 12 Sep 2023
Blink: Link Local Differential Privacy in Graph Neural Networks via Bayesian Estimation Xiaochen Zhu Vincent Y. F. Tan Xiaokui Xiao 32 9 0 06 Sep 2023
Towards Unsupervised Graph Completion Learning on Graphs with Features and Structure Missing Sichao Fu Qinmu Peng Yang He Baokun Du Xinge You 31 2 0 06 Sep 2023
Rank Collapse Causes Over-Smoothing and Over-Correlation in Graph Neural Networks Andreas Roth Thomas Liebig 56 13 0 31 Aug 2023
Domain-adaptive Message Passing Graph Neural Network X. Shen Shirui Pan K. Choi Xiaoping Zhou 40 16 0 31 Aug 2023
Cached Operator Reordering: A Unified View for Fast GNN Training Julia Bazinska Andrei Ivanov Tal Ben-Nun Nikoli Dryden Maciej Besta Siyuan Shen Torsten Hoefler GNN 27 3 0 23 Aug 2023
Label-based Graph Augmentation with Metapath for Graph Anomaly Detection Hwan Kim Junghoon Kim Byung Suk Lee Sungsu Lim 32 0 0 21 Aug 2023
GiGaMAE: Generalizable Graph Masked Autoencoder via Collaborative Latent Space Reconstruction Yucheng Shi Yushun Dong Qiaoyu Tan Jundong Li Ninghao Liu 53 25 0 18 Aug 2023
Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learning Tianmeng Yang Min Zhou Yujing Wang Zhe-Min Lin Lujia Pan Bin Cui Yu Tong AAML 39 3 0 17 Aug 2023
Language is All a Graph Needs Ruosong Ye Caiqi Zhang Runhui Wang Shuyuan Xu Yongfeng Zhang AI4CE 68 151 0 14 Aug 2023
Learning on Graphs with Out-of-Distribution Nodes Yunho Song Donglin Wang OODD 21 39 0 13 Aug 2023
Differentially Private Graph Neural Network with Importance-Grained Noise Adaption Yuxin Qi Xi Lin Jun Wu 46 0 0 09 Aug 2023
Exploiting On-chip Heterogeneity of Versal Architecture for GNN Inference Acceleration Paul Chen Pavan Manjunath Sasindu Wijeratne Bingyi Zhang Viktor Prasanna GNN 21 8 0 04 Aug 2023
Evaluating Link Prediction Explanations for Graph Neural Networks Claudio Borile Alan Perotti Andre' Panisson FAtt 46 2 0 03 Aug 2023
SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning Keyu Duan Qian Liu Tat-Seng Chua Shuicheng Yan Wei Tsang Ooi Qizhe Xie Junxian He 40 57 0 03 Aug 2023
MUSE: Multi-View Contrastive Learning for Heterophilic Graphs Mengyi Yuan Minjie Chen Xiangci Li SSL 39 8 0 29 Jul 2023
MARIO: Model Agnostic Recipe for Improving OOD Generalization of Graph Contrastive Learning Yun Zhu Haizhou Shi Zhenshuo Zhang Siliang Tang 33 8 0 24 Jul 2023
Homophily-Driven Sanitation View for Robust Graph Contrastive Learning Yulin Zhu Xing Ai Yevgeniy Vorobeychik Kai Zhou AAML 26 0 0 24 Jul 2023
Learning Adaptive Neighborhoods for Graph Neural Networks Avishkar Saha Oscar Alejandro Mendez Maldonado Chris Russell Richard Bowden GNN 16 4 0 18 Jul 2023
Disentangling Node Attributes from Graph Topology for Improved Generalizability in Link Prediction Ayan Chatterjee Robin Walters G. Menichetti Tina Eliassi-Rad AI4CE 32 2 0 17 Jul 2023
Automated Polynomial Filter Learning for Graph Neural Networks Wendi Yu Zhichao Hou Xiaorui Liu 24 0 0 16 Jul 2023
Extended Graph Assessment Metrics for Graph Neural Networks Tamara T. Mueller Sophie Starck Leonhard F. Feiner Kyriaki-Margarita Bintsi Daniel Rueckert Georgios Kaissis 35 1 0 13 Jul 2023
Differentially Private Decoupled Graph Convolutions for Multigranular Topology Protection Eli Chien Wei-Ning Chen Chao Pan Pan Li Ayfer Özgür O. Milenkovic 41 12 0 12 Jul 2023
Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs Zhikai Chen Haitao Mao Hang Li Wei Jin Haifang Wen ... Shuaiqiang Wang Dawei Yin Wenqi Fan Hui Liu Jiliang Tang AI4CE 57 266 0 07 Jul 2023
A Survey on Graph Classification and Link Prediction based on GNN Xingyu Liu Juan Chen Q. Wen GNN 29 11 0 03 Jul 2023
Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals Tingting Dan Jiaqi Ding Ziquan Wei S. Kovalsky Minjeong Kim Won Hwa Kim Guorong Wu DiffM 24 6 0 01 Jul 2023
SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph Generation Stratis Limnios Praveen Selvaraj Mihai Cucuringu Carsten Maple Gesine Reinert Andrew Elliott DiffM 41 7 0 29 Jun 2023
Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions Lukas Gosch Simon Geisler Daniel Sturm Bertrand Charpentier Daniel Zügner Stephan Günnemann AAML GNN 27 29 0 27 Jun 2023
Contrastive Meta-Learning for Few-shot Node Classification Song Wang Zhen Tan Huan Liu Jundong Li 36 15 0 27 Jun 2023
Torsion Graph Neural Networks Cong Shen Xiang Liu Jiawei Luo Kelin Xia 42 0 0 23 Jun 2023
Directional diffusion models for graph representation learning Run Yang Yuling Yang Fan Zhou Qiang Sun DiffM 25 13 0 22 Jun 2023
Structure-Aware DropEdge Towards Deep Graph Convolutional Networks Jiaqi Han Wen-bing Huang Yu Rong Tingyang Xu Gang Hua Junzhou Huang 33 5 0 21 Jun 2023
Contrastive Disentangled Learning on Graph for Node Classification Xiaojuan Zhang Jun Fu Shuang Li SSL 28 1 0 20 Jun 2023
Variational Disentangled Graph Auto-Encoders for Link Prediction Jun Fu Xiaojuan Zhang Shuang Li Dali Chen CML DRL 35 3 0 20 Jun 2023
GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks Wentao Zhao Qitian Wu Chenxiao Yang Junchi Yan 26 12 0 20 Jun 2023
P-tensors: a General Formalism for Constructing Higher Order Message Passing Networks Tianyi Sun Andrew R. Hands Risi Kondor 27 2 0 19 Jun 2023
Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions Fang Li Yi Nian Zenan Sun Cui Tao LM&MA OOD AI4TS AI4CE 33 5 0 18 Jun 2023
Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking Juanhui Li Harry Shomer Haitao Mao Shenglai Zeng Yao Ma Neil Shah Jiliang Tang Dawei Yin 81 51 0 18 Jun 2023