There has been a recent surge of interest in designing Graph Neural Networks (GNNs) for semi-supervised learning tasks. Unfortunately this work has assumed that the nodes labeled for use in training were selected uniformly at random (i.e. are an IID sample). However in many real world scenarios gathering labels for graph nodes is both expensive and inherently biased -- so this assumption can not be met. GNNs can suffer poor generalization when this occurs, by overfitting to superfluous regularities present in the training data. In this work we present a method, Shift-Robust GNN (SR-GNN), designed to account for distributional differences between biased training data and the graph's true inference distribution. SR-GNN adapts GNN models for the presence of distributional shifts between the nodes which have had labels provided for training and the rest of the dataset. We illustrate the effectiveness of SR-GNN in a variety of experiments with biased training datasets on common GNN benchmark datasets for semi-supervised learning, where we see that SR-GNN outperforms other GNN baselines by accuracy, eliminating at least (~40%) of the negative effects introduced by biased training data. On the largest dataset we consider, ogb-arxiv, we observe an 2% absolute improvement over the baseline and reduce 30% of the negative effects.
When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；The Hong Kong University of Science and Technology（香港科技大学）；East China Normal University（华东师范大学）；University of Michigan（密歇根大学）
All Nodes are created Not Equal: Node-Specific Layer Aggregation and Filtration for GNN
University of Science and Technology of China（中国科学技术大学）；Tongji University（同济大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；University of Oxford（牛津大学）；Griffith University（格里菲斯大学）
MegaGraph: Towards Efficient Training of Large-Scale Graph Transformers with Automated Hybrid Parallelism
Beihang University（北京航空航天大学）；Qingdao Research Institute, Beihang University（北京航空航天大学青岛研究院）；State Key Laboratory of Complex and Critical Software Environment, Beihang University（北京航空航天大学复杂关键软件环境国家重点实验室）
Explainable Fuzzy GNNs for Leak Detection in Water Distribution Networks
Jheronimus Academy of Data Science, Eindhoven University of Technology, Eindhoven, The Netherlands University of Bari Aldi Moro, Bari, Italy IHE Institute for Water Education, Delft, The Netherlands University of Cartagena, Cartagena, Spain School of Industrial Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
Harvard Medical School（哈佛医学院）；Harvard College（哈佛学院）；Broad Institute of MIT and Harvard（麻省理工学院和哈佛大学布罗德研究所）；Harvard Data Science Initiative（哈佛数据科学研究院）；Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University（哈佛大学肯普纳自然与人工智能研究学院）
BLIS-Net: Classifying and Analyzing Signals on Graphs
Yale University（耶鲁大学）；Harvey Mudd College（哈维穆德学院）；Yale School of Medicine（耶鲁大学医学院）；University of Colorado Boulder（科罗拉多大学博尔德分校）；Boise State University（博伊西州立大学）
G-PARC: Graph-Physics Aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics on Unstructured Meshes
School of Data Science, University of Virginia（数据科学学院，弗吉尼亚大学）；University of Virginia（弗吉尼亚大学）；Department of Engineering Systems and Environment, University of Virginia（工程系统与环境系，弗吉尼亚大学）；Department of Mechanical and Aerospace Engineering, University of Virginia（机械与航空航天工程系，弗吉尼亚大学）；University of Iowa（爱荷华大学）
Forget and Explain: Transparent Verification of GNN Unlearning
Chung-Ang University Department of Smart Cities Seoul Republic of Korea（Chung-Ang大学智慧城市系 首尔 大韩民国）；Chung-Ang University Department of Computer Science（Chung-Ang大学计算机科学系）；Chung-Ang University（Chung-Ang大学）
Rethinking Graph Neural Networks for Anomaly Detection
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Hong Kong University of Science and Technology（香港科技大学）；Stanford University（斯坦福大学）
Time-Aware and Transition-Semantic Graph Neural Networks for Interpretable Predictive Business Process Monitoring
College of Computing and Mathematical Sciences, Khalifa University（卡利法大学计算与数学科学学院）；Department of Computer Science, Università degli Studi di Milano（米兰大学计算机科学系）
Few-Sample Traffic Prediction with Graph Networks using Locale as Relational Inductive Biases
The Hong Kong Polytechnic University（香港理工大学）；University of Hong Kong（香港大学）；The Hong Kong Polytechnic University Shenzhen Research Institute（香港理工大学深圳研究院）；Research Institute for Sustainable Urban Development, The Hong Kong Polytechnic University（香港理工大学可持续城市发展研究院）
FIGNN: Feature-Specific Interpretability for Graph Neural Network Surrogate Models
Department of Mechanical Engineering, Pennsylvania State University, University Park, 16802, PA, USA（宾夕法尼亚州立大学机械工程系）；College of Information Sciences and Technology, Pennsylvania State University, University Park, 16802, PA, USA（宾夕法尼亚州立大学信息科学与技术学院）；Power Systems Division, Argonne National Laboratory, Lemont, 60439, IL, USA（阿贡国家实验室电力系统分部）
