As powerful tools for representation learning on graphs, graph neural networks (GNNs) have facilitated various applications from drug discovery to recommender systems. Nevertheless, the effectiveness of GNNs is immensely challenged by issues related to data quality, such as distribution shift, abnormal features and adversarial attacks. Recent efforts have been made on tackling these issues from a modeling perspective which requires additional cost of changing model architectures or re-training model parameters. In this work, we provide a data-centric view to tackle these issues and propose a graph transformation framework named GTrans which adapts and refines graph data at test time to achieve better performance. We provide theoretical analysis on the design of the framework and discuss why adapting graph data works better than adapting the model. Extensive experiments have demonstrated the effectiveness of GTrans on three distinct scenarios for eight benchmark datasets where suboptimal data is presented. Remarkably, GTrans performs the best in most cases with improvements up to 2.8%, 8.2% and 3.8% over the best baselines on three experimental settings. Code is released at https://github.com/ChandlerBang/GTrans.
Empowering machine learning models with contextual knowledge for enhancing the detection of eating disorders in social media posts
Universidad de León（莱昂大学）；Leibniz University of Hannover（汉诺威莱布尼茨大学）；L3S Research Center（L3S研究中心）；TIB Leibniz Information Centre for Science and Technology（莱布尼茨科学与技术信息中心）；Bakken and Baeck GmbH（Bakken and Baeck有限公司）
Empowering Wildlife Guardians: An Equitable Digital Stewardship and Reward System for Biodiversity Conservation using Deep Learning and 3/4G Camera Traps
Liverpool John Moores University（利物浦约翰摩尔斯大学）；Gap Africa Projects（Gap非洲项目）；Welgevonden Game Reserve（韦尔格翁登禁猎区）；Borneo Futures（婆罗洲未来组织）；Czech Technical University（捷克技术大学）
BudgetThinker: Empowering Budget-aware LLM Reasoning with Control Tokens
Institute for AI Industry Research (AIR) Tsinghua University（人工智能产业研究院（AIR）清华大学）；Global Innovation Exchange & Department of Automation Tsinghua University（全球创新交流中心及自动化系 清华大学）
ACT: Empowering Decision Transformer with Dynamic Programming via Advantage Conditioning
National Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术全国重点实验室）；School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）
Empowering VLMs for Few-Shot Multimodal Time Series Classification via Tailored Agentic Reasoning
Sun Yat-sen University（中山大学）；Xiaomi Corporation（小米公司）；University of Science and Technology of China（中国科学技术大学）；National University of Singapore（新加坡国立大学）
Adaptive Recurrent Message Passing for Test Time Computing on Graphs
State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所人工智能安全国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）
Enhancing Adversarial Robustness via Test-time Transformation Ensembling
King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）；Center for Research and Formation in Artificial Intelligence, Universidad de los Andes（安第斯大学人工智能研究与培训中心）
