A Hybrid TGN-SEAL Model for Dynamic Graph Link Prediction
Department of Computer Engineering, Sharif University of Technology（谢尔万大学计算机工程系）；Tehran Institute for Advanced Studies, Khatam University（泰赫兰高级研究院，卡塔姆大学）
Predicting links in sparse, continuously evolving networks is a central challenge in network science. Conventional heuristic methods and deep learning models, including Graph Neural Networks (GNNs), are typically designed for static graphs and thus struggle to capture temporal dependencies. Snapshot-based techniques partially address this issue but often encounter data sparsity and class imbalance, particularly in networks with transient interactions such as telecommunication call detail records (CDRs). Temporal Graph Networks (TGNs) model dynamic graphs by updating node embeddings over time; however, their predictive accuracy under sparse conditions remains limited. In this study, we improve the TGN framework by extracting enclosing subgraphs around candidate links, enabling the model to jointly learn structural and temporal information. Experiments on a sparse CDR, email, message dataset show that our approach increases average precision by at least 2% over standard TGNs, demonstrating the advantages of integrating local topology for robust link prediction in dynamic networks.
Leveraging Vulnerabilities in Temporal Graph Neural Networks via Strategic High-Impact Assaults
Bowling Green State University（博伊尔格林州立大学）；University of Houston-Clear Lake（休斯顿-克莱湖大学）；Grand Valley State University（格兰德山谷州立大学）；University of Maryland at Baltimore County（马里兰大学巴尔的摩县分校）
Are Large Language Models Good Temporal Graph Learners?
Mila - Quebec AI Institute（魁北克AI研究所）；School of Computer Science, McGill University（麦吉尔大学计算机科学学院）；University of Cambridge（剑桥大学）；DIRO, Université de Montréal（蒙特利尔大学DIRO研究所）；CIFAR AI Chair（CIFAR人工智能主席）；New Jersey Institute of Technology（新泽西理工学院）；University of Oxford（牛津大学）；AITHYRA
Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection
Texas A&M University（德州农工大学）；The Hong Kong Polytechnic University（香港理工大学）；University of Georgia（佐治亚大学）；Rice University（莱斯大学）；Samsung Electronics America（三星电子美国公司）；Samsung Electronics（三星电子）
Graph Neural Networks Based Analog Circuit Link Prediction
HDU-ITMO Joint Institute, Hangzhou Dianzi University（杭州电子科技大学信息处理联合研究所）；Intelligent Information Processing Laboratory, Hangzhou Dianzi University（杭州电子科技大学智能信息处理实验室）；Innovation Center for Electronic Design Automation Technology, Hangzhou Dianzi University（杭州电子科技大学电子设计自动化技术创新中心）；School of Computer Science and Technology, South China Business College, Guangdong University of Foreign Studies（广东外语外贸大学南方商学院计算机科学与技术学院）；Department of Computer Science and Technology, University of Cambridge（剑桥大学计算机科学与技术系）
Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning
Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；Beijing Institute for General Artificial Intelligence（北京通用人工智能研究院）；Department of Computer Science, Purdue University（普渡大学计算机科学系）；Facebook AI（Facebook 人工智能）
UTG: Towards a Unified View of Snapshot and Event Based Models for Temporal Graphs
Mila - Quebec AI Institute（米拉-魁北克人工智能研究所）；School of Computer Science, McGill University（麦吉尔大学计算机学院）；CIFAR AI Chair（加拿大高级研究所人工智能主席项目）；DIRO, Université de Montréal（蒙特利尔大学计算机科学与运筹学系）；VantAI（万泰人工智能公司）
