• case-insensitive prefix search: default
  e.g., sig matches "SIGIR" as well as "signal"
• exact word search: append dollar sign ($) to word
  e.g., graph$ matches "graph", but not "graphics"
• boolean and: separate words by space
  e.g., codd model
• boolean or: connect words by pipe symbol (|)
  e.g., graph|network

Update May 7, 2017: Please note that we had to disable the phrase search operator (.) and the boolean not operator (-) due to technical problems. For the time being, phrase search queries will yield regular prefix search result, and search terms preceded by a minus will be interpreted as regular (positive) search terms.

found 49 matches

• 2024
• 
  Xin Li, Hao Wei, Yu Ding:
  PHGNN: Pre-Training Heterogeneous Graph Neural Networks. IEEE Access 12: 135411-135418 (2024)
• 
  Zhenxing Wang, Zhongyu Wei:
  PT-KGNN: A framework for pre-training biomedical knowledge graphs with graph neural networks. Comput. Biol. Medicine 178: 108768 (2024)
• 
  Yichun Li, Jin Huang, Weihao Yu, Tinghua Zhang:
  Neighborhood-enhanced contrast for pre-training graph neural networks. Neural Comput. Appl. 36(8): 4195-4205 (2024)
• 
  Gregor Donabauer, Udo Kruschwitz:
  Challenges in Pre-Training Graph Neural Networks for Context-Based Fake News Detection: An Evaluation of Current Strategies and Resource Limitations. LREC/COLING 2024: 2997-3004
• 
  Yuanchen Bei, Hao Xu, Sheng Zhou, Huixuan Chi, Haishuai Wang, Mengdi Zhang, Zhao Li, Jiajun Bu:
  CPDG: A Contrastive Pre-Training Method for Dynamic Graph Neural Networks. ICDE 2024: 1199-1212
• 
  Yiwei Dai, Mingchen Sun, Xin Wang:
  Pre-training Graph Neural Networks via Weighted Meta Learning. IJCNN 2024: 1-8
• 
  Yasufumi Sakai, Naoki Matsumura, Atsuki Inoue, Hiroshi Kawaguchi, Thang Dang, Atsushi Ishikawa, Árni Björn Höskuldsson, Egill Skúlason:
  Active Learning for Graph Neural Networks Training in Catalyst Energy Prediction. IJCNN 2024: 1-8
• 
  Wenying Duan, Tianxiang Fang, Hong Rao, Xiaoxi He:
  Pre-Training Identification of Graph Winning Tickets in Adaptive Spatial-Temporal Graph Neural Networks. KDD 2024: 701-712
• 
  Mingkai Lin, Wenzhong Li, Xiaobin Hong, Sanglu Lu:
  Scalable Multi-Source Pre-training for Graph Neural Networks. ACM Multimedia 2024: 1292-1301
• 
  Qingqing Ge, Zeyuan Zhao, Yiding Liu, Anfeng Cheng, Xiang Li, Shuaiqiang Wang, Dawei Yin:
  PSP: Pre-training and Structure Prompt Tuning for Graph Neural Networks. ECML/PKDD (5) 2024: 423-439
• 
  Giuseppe Spillo, Francesco Bottalico, Cataldo Musto, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro:
  Evaluating Content-based Pre-Training Strategies for a Knowledge-aware Recommender System based on Graph Neural Networks. UMAP 2024: 165-171
• 
  Gregor Donabauer, Udo Kruschwitz:
  Challenges in Pre-Training Graph Neural Networks for Context-Based Fake News Detection: An Evaluation of Current Strategies and Resource Limitations. CoRR abs/2402.18179 (2024)
• 
  Wenying Duan, Tianxiang Fang, Hong Rao, Xiaoxi He:
  Pre-Training Identification of Graph Winning Tickets in Adaptive Spatial-Temporal Graph Neural Networks. CoRR abs/2406.08287 (2024)
• 
  Zihan Pengmei, Zhengyuan Shen, Zichen Wang, Marcus Collins, Huzefa Rangwala:
  Pushing the Limits of All-Atom Geometric Graph Neural Networks: Pre-Training, Scaling and Zero-Shot Transfer. CoRR abs/2410.21683 (2024)
• 
  Ivan Kralj, Lodovico Giaretta, Gordan Jezic, Ivana Podnar Zarko, Sarunas Girdzijauskas:
  Semi-decentralized Training of Spatio-Temporal Graph Neural Networks for Traffic Prediction. CoRR abs/2412.03188 (2024)
• 
  Van Thuy Hoang, O-Joun Lee:
  Pre-training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck. CoRR abs/2412.15589 (2024)
• 2023
• 
  Sitao Luan, Mingde Zhao, Xiao-Wen Chang, Doina Precup:
  Training Matters: Unlocking Potentials of Deeper Graph Convolutional Neural Networks. COMPLEX NETWORKS (1) 2023: 49-60
• 
  Jun Xia, Chengshuai Zhao, Bozhen Hu, Zhangyang Gao, Cheng Tan, Yue Liu, Siyuan Li, Stan Z. Li:
  Mole-BERT: Rethinking Pre-training Graph Neural Networks for Molecules. ICLR 2023
• 
  Zhonghang Li, Lianghao Xia, Yong Xu, Chao Huang:
  GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks. NeurIPS 2023
• 
  Jiarong Xu, Renhong Huang, Xin Jiang, Yuxuan Cao, Carl Yang, Chunping Wang, Yang Yang:
  Better with Less: A Data-Active Perspective on Pre-Training Graph Neural Networks. NeurIPS 2023
• 
  Jun Yin, Chaozhuo Li, Hao Yan, Jianxun Lian, Senzhang Wang:
  Train Once and Explain Everywhere: Pre-training Interpretable Graph Neural Networks. NeurIPS 2023
• 
  Zemin Liu, Xingtong Yu, Yuan Fang, Xinming Zhang:
  GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks. WWW 2023: 417-428
• 
  Zemin Liu, Xingtong Yu, Yuan Fang, Xinming Zhang:
  GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks. CoRR abs/2302.08043 (2023)
• 
  Yuanchen Bei, Hao Xu, Sheng Zhou, Huixuan Chi, Haishuai Wang, Mengdi Zhang, Zhao Li, Jiajun Bu:
  CPDG: A Contrastive Pre-Training Method for Dynamic Graph Neural Networks. CoRR abs/2307.02813 (2023)
• 
  Stone Yun, Alexander Wong:
  GHN-QAT: Training Graph Hypernetworks to Predict Quantization-Robust Parameters of Unseen Limited Precision Neural Networks. CoRR abs/2309.13773 (2023)
• 
  Jiarong Xu, Renhong Huang, Xin Jiang, Yuxuan Cao, Carl Yang, Chunping Wang, Yang Yang:
  Better with Less: A Data-Active Perspective on Pre-Training Graph Neural Networks. CoRR abs/2311.01038 (2023)
• 
  Zhonghang Li, Lianghao Xia, Yong Xu, Chao Huang:
  GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks. CoRR abs/2311.04245 (2023)
• 2022
• 
  Yahui Long, Min Wu, Yong Liu, Yuan Fang, Chee Keong Kwoh, Jinmiao Chen, Jiawei Luo, Xiaoli Li:
  Pre-training graph neural networks for link prediction in biomedical networks. Bioinform. 38(8): 2254-2262 (2022)
• 
  Ke-Jia Chen, Jiajun Zhang, Linpu Jiang, Yunyun Wang, Yuxuan Dai:
  Pre-training on dynamic graph neural networks. Neurocomputing 500: 679-687 (2022)
• 
  Jong Youl Choi, Pei Zhang, Kshitij Mehta, Andrew E. Blanchard, Massimiliano Lupo Pasini:
  Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules. J. Cheminformatics 14(1): 70 (2022)

skipping 19 more matches