Source code summarization aims to generate natural language summaries from structured code snippets for better understanding code functionalities. However, automatic code summarization is challenging due to the complexity of the source code and the language gap between the source code and natural language summaries. Most previous approaches either rely on retrieval-based (which can take advantage of similar examples seen from the retrieval database, but have low generalization performance) or generation-based methods (which have better generalization performance, but cannot take advantage of similar examples). This paper proposes a novel retrieval-augmented mechanism to combine the benefits of both worlds. Furthermore, to mitigate the limitation of Graph Neural Networks (GNNs) on capturing global graph structure information of source code, we propose a novel attention-based dynamic graph to complement the static graph representation of the source code, and design a hybrid message passing GNN for capturing both the local and global structural information. To evaluate the proposed approach, we release a new challenging benchmark, crawled from diversified large-scale open-source C projects (total 95k+ unique functions in the dataset). Our method achieves the state-of-the-art performance, improving existing methods by 1.42, 2.44 and 1.29 in terms of BLEU-4, ROUGE-L and METEOR.
Graph Mixture of Experts and Memory-augmented Routers for Multivariate Time Series Anomaly Detection
University of Science and Technology of China（中国科学技术大学）；Institute of Plasma Physics, Hefei Institutes of Physical Science, Chinese Academy of Sciences（中国科学院合肥物质科学研究院等离子体物理研究所）
KAT-GNN: A Knowledge-Augmented Temporal Graph Neural Network for Risk Prediction in Electronic Health Records
Institute of Computer Science and Engineering, National Yang Ming Chiao Tung University（计算机科学与工程研究所，国立阳明交通大学）；School of Medicine, National Tsing Hua University（医学院，国立清华大学）；Department of Computer Science, National Yang Ming Chiao Tung University（计算机科学系，国立阳明交通大学）；Computational Health Informatics Program, Boston Children’s Hospital（计算健康信息学计划，波士顿儿童医院）
Institut de Mathématiques de Toulouse（图卢兹数学研究所）；University of the Basque Country（巴斯克大学）；IKERBASQUE（巴斯克科学基金会）；Institut Polytechnique de Paris（巴黎综合理工学院）；INRIA, Projet Regalia（法国国家信息与自动化研究所，Regalia项目）
Mitigating the Structural Bias in Graph Adversarial Defenses
Department of Electrical Engineering, City University of Hong Kong（香港城市大学电子工程系）；School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；School of Software Engineering, Sun Yat-sen University（中山大学软件工程学院）
Node Classification via Simplicial Interaction with Augmented Maximal Clique Selection
Department of Big Data Convergence, Chonnam National University（convergence大数据部门，全州国民大学）；Mitch Daniels School of Business, Purdue University（Daniels商学院，普渡大学）
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural Networks
Faculty of Engineering and Information Technology, University of Technology Sydney（悉尼科技大学工程与信息技术学院）；School of Computing and Information Systems, Singapore Management University（新加坡管理大学计算与信息系统学院）
Sketch-Augmented Features Improve Learning Long-Range Dependencies in Graph Neural Networks
Department of Computer Science, University of Chicago（芝加哥大学计算机科学系）；Leadership Computing Facility, Argonne National Laboratory（阿贡国家实验室领导计算设施）；Department of Statistics, University of Chicago（芝加哥大学统计系）；NSF-Simons National Institute for Theory and Mathematics in Biology（国家理论与生物数学研究所）
本文提出将随机化全局嵌入（Sketched Random Features）注入标准图神经网络，以缓解过度挤压、过度平滑和表达力受限问题，从而高效捕获长程依赖并在真实图任务上持续提升性能。
GLaDiGAtor: Language-Model-Augmented Multi-Relation Graph Learning for Predicting Disease-Gene Associations
Biological Data Science Lab, Dept. of Computer Engineering, Hacettepe University（生物数据科学实验室，计算机工程系，哈切泰佩大学）；Dept. of Bioinformatics, Graduate School of Health Sciences, Hacettepe University（生物信息学系，健康科学研究生院，哈切泰佩大学）；Dept. of Health Informatics, Institute of Informatics, Hacettepe University（健康信息学系，信息学院，哈切泰佩大学）
Multimodal Audio-Visual Information Fusion using Canonical-Correlated Graph Neural Network for Energy-Efficient Speech Enhancement
University of Wolverhampton（伍尔弗汉普顿大学）；São Paulo State University（圣保罗州立大学）；TECNALIA（泰克纳利亚研究中心）；Basque Research & Technology Alliance（巴斯克研究与技术联盟）；University of the Basque Country（巴斯克大学）；Edinburgh Napier University（爱丁堡龙比亚大学）
GALAX: Graph-Augmented Language Model for Explainable Reinforcement-Guided Subgraph Reasoning in Precision Medicine
I2DB, Washington University School of Medicine（I2DB，华盛顿大学医学学院）；Department of Computer Science and Engineering, Washington University in St. Louis（计算机科学与工程系，华盛顿大学圣路易斯分校）；Department of Genetics, Washington University School of Medicine（遗传学系，华盛顿大学医学学院）
Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression
Department of Electrical and Systems Engineering, University of Pennsylvania（宾夕法尼亚大学电气与系统工程系）；Department of Biostatistics and Bioinformatics, Duke University（杜克大学生物统计与生物信息学系）；Johns Hopkins Applied Research Laboratory (APL)（约翰霍普金斯应用研究实验室）
Universally Invariant Learning in Equivariant GNNs
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学明德学院）；Beijing Key Laboratory of Research on Large Models and Intelligent Governance（北京大模型与智能治理重点实验室）；Engineering Research Center of Next-Generation Intelligent Search and Recommendation, MOE（下一代智能搜索与推荐工程技术研究中心）；DAMO Academy, Alibaba Group（阿里达摩院）；Hupan Lab（虎斑实验室）
