• Best Paper Award I-Hung Hsu, Xiao Guo, Premkumar Natarajan and Nanyun Peng. Discourse-level Relation Extraction via Graph Pooling.[Link]
  • Best Student Paper Award Yongqiang Mao, Xian Sun, Kaiqiang Chen, Wenhui Diao, Zonghao Guo, Xiaonan Lu and Kun Fu. Semantic Segmentation for Point Cloud Scenes via Dilated Graph Feature Aggregation and Pyramid Decoders.[Link]
  • Jian Zhang, Minghao Zhao, Runze Wu and Qi Xuan. Center-Oriented Attentive Temporal Pooling for Link Prediction on Dynamic Networks.[Link]
  • Zeyu Zhang and Yulong Pei. A Comparative Study on Robust Graph Neural Networks to Structural Noises.[Link]
  • Mohammadamin Tavakoli, Alexander Shmakov, Francesco Ceccarelli and Pierre Baldi. Rxn Hypergraph: a Hypergraph Attention Model for Chemical Reaction Representation.[Link]
  • Eunkyu Oh, Taehun Kim, Minsoo Kim, Yunhu Ji and Sushil Khyalia. SR-GCL: Session-Based Recommendation with Global Context Enhanced Augmentation in Contrastive Learning.[Link]
  • Lai Wei, Atia Hamidi Zadeh and Martin Ester. Combining Graph Attention Mechanism and PageRank to Learn Graph-level Representations.[Link]
  • Guojing Cong, Anshul Gupta, Rodrigo Newmann, Breanndan O Conchuir, Maira de Bayser and Mathias Steiner. Prediction of CO2 Adsorption in Nano-Pores with Graph Neural Networks.[Link]
  • Yinkai Wang, Aowei Ding, Kaiyi Guan, Shixi Wu and Yuanqi Du. Graph-based Ensemble Machine Learning for Student Performance Prediction.[Link]
  • Soyeon Han, Zihan Yuan, Kunze Wang, Siqu Long and Josiah Poon. Understanding Graph Convolutional Networks for Text Classification.[Link]
  • Cole Hawkins, Vassilis Ioannidis, Soji Adeshina and George Karypis. Scalable Consistency Training for Graph Neural Networks via Self-Ensemble Self-Distillation.[Link]
  • Chenyang Qiu, Zhaoci Huang, Wenzhe Xu and Hui-Jia Li. VGAER: graph neural network reconstruction based community detection.[Link]
  • Xu Wang, Huan Zhao, Weiwei Tu, Hao Li, Yu Sun and Xiaochen Bo. Graph Neural Networks for Double-Strand DNA Breaks Prediction.[Link]
  • Donghan Yu and Yiming Yang. Improving Hyper-Relational Knowledge Graph Completion.[Link]
  • MNaganand Yadati, Tarun Kumar, Deepak Maurya, Partha Talukdar and Balaraman Ravindran. HEAL: Embedding Attributed Multi-layer Hypergraphs.[Link]
  • Inder Pal Singh, Oyebade Oyedotun, Enjie Ghorbel and Djamila Aouada. IML-GCN: Improved Multi-Label Graph Convolutional Network for Efficient yet Precise Image Classification.[Link]
  • Guannan Lou, Yuze Liu, Tiehua Zhang and Xi Zheng. STFL: A Spatial-Temporal Federated Learning Framework for Graph Neural Networks.[Link]
  • Kiarash Zahirnia, Oliver Schulte, Ke Li, Ankita Sakhuja and Parmis Naddaf. Deep Learning of Latent Edge Types from Relational Data.[Link]
  • Sheng Hu, Ichigaku Takigawa and Chuan Xiao. Edit-Aware Generative Molecular Graph Autocompletion for Scaffold Input.[Link]
  • Yang Huang and Jiabei Hu. Light Hypergraph Collaborative Filtering based on high-order neighbor convolution.[Link]
  • Raiyan Khan, Thanh Nguyen and Srinivasan Sengamedu. Hyperbolic Representations of Source Code.[Link]
  • Oliver Schulte, Parmis Naddaf, Xia Hu and Manfred Jaeger. Deep Variational Inference for Inductive Link Prediction.[Link]