GraphSearchNet: Enhancing GNNs via Capturing Global Dependencies for Semantic Code Search
Nanyang Technological University（南洋理工大学）；Singapore Management University（新加坡管理大学）；University of Alberta（阿尔伯塔大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）
Shangqing Liu, Xiaofei Xie, Jingkai Siow, Lei Ma, Guozhu Meng, Yang Liu
Code search aims to retrieve accurate code snippets based on a natural language query to improve software productivity and quality. With the massive amount of available programs such as (on GitHub or Stack Overflow), identifying and localizing the precise code is critical for the software developers. In addition, Deep learning has recently been widely applied to different code-related scenarios, e.g., vulnerability detection, source code summarization. However, automated deep code search is still challenging since it requires a high-level semantic mapping between code and natural language queries. Most existing deep learning-based approaches for code search rely on the sequential text i.e., feeding the program and the query as a flat sequence of tokens to learn the program semantics while the structural information is not fully considered. Furthermore, the widely adopted Graph Neural Networks (GNNs) have proved their effectiveness in learning program semantics, however, they also suffer the problem of capturing the global dependencies in the constructed graph, which limits the model learning capacity. To address these challenges, in this paper, we design a novel neural network framework, named GraphSearchNet, to enable an effective and accurate source code search by jointly learning the rich semantics of both source code and natural language queries. Specifically, we propose to construct graphs for the source code and queries with bidirectional GGNN (BiGGNN) to capture the local structural information of the source code and queries. Furthermore, we enhance BiGGNN by utilizing the multi-head attention module to supplement the global dependencies that BiGGNN missed to improve the model learning capacity. The extensive experiments on Java and Python programming language from the public benchmark CodeSearchNet confirm that GraphSearchNet outperforms current state-of-the-art works.
Binarizing Physics-Inspired GNNs for Combinatorial Optimization
Department of Computer Science, Faculty of Electrical Engineering, Czech Technical University（计算机科学系，电气工程学院，捷克技术大学）；Quantum Technologies Group, HSBC Labs, Singapore（量子技术组，汇丰实验室，新加坡）；Archimedes Research Unit on AI, Data Science and Algorithms, Athena Research Center（人工智能、数据科学与算法研究单元，阿提卡研究中心）
Discourse-level Relation Extraction via Graph Pooling
Information Sciences Institute, University of Southern California（南加州大学信息科学研究所）；Michigan State University（密歇根州立大学）；University of California, Los Angeles（加州大学洛杉矶分校）
Cross-attentive Cohesive Subgraph Embedding to Mitigate Oversquashing in GNNs
Department of Computer Science, Georgia State University（佐治亚州立大学计算机科学系）；Department of Data Science and Analytics, Kennesaw State University（肯尼索州立大学数据科学与分析系）
GNNs for Time Series Anomaly Detection: An Open-Source Framework and a Critical Evaluation
Facultad de Ingeniería, Universidad de la República（拉普拉塔大学工程学院）；Pento；Centro Interdisciplinario en Ciencia de Datos y Aprendizaje Automático (CICADA), Universidad de la República（跨学科数据科学与自动学习中心（CICADA），拉普拉塔大学）
PGT-I: Scaling Spatiotemporal GNNs with Memory-Efficient Distributed Training
University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Argonne National Laboratory（阿贡国家实验室）；Arizona State University（亚利桑那州立大学）；Sandia National Laboratories（桑塔纳国家实验室）
Graph Generation Powered with LLMs for Boosting Multivariate Time-Series Representation Learning
Institute for Infocomm Research, A ∗ STAR, Singapore（信息通信研究所，A*STAR，新加坡）；School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore（电气电子工程学院，南洋理工大学，新加坡）；College of Computing and Data Science, Nanyang Technological University, Singapore（计算与数据科学学院，南洋理工大学，新加坡）
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
Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networks
School of Software Technology, Zhejiang University（浙江大学软件学院）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新区（滨江）区块链与数据安全研究院）；Big Graph Center, School of Computer and Computing Science, Hangzhou City University（杭州城市大学计算机与计算科学学院大图中心）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
Towards Multiscale Graph-based Protein Learning with Geometric Secondary Structural Motifs
Department of Mathematics and Scientific Computing and Imaging (SCI) Institute University of Utah（数学与科学计算与成像研究所（SCI）大学）；Department of Mathematics, UCLA（数学系，加州大学洛杉矶分校）；Department of Mathematics, UC Davis（数学系，加州大学戴维斯分校）
GREPO: A Benchmark for Graph Neural Networks on Repository-Level Bug Localization
Institute for Artificial Intelligence, Peking University（人工智能研究院，北京大学）；School of Intelligence Science and Technology, Peking University（智能科学与技术学院，北京大学）；College of Intelligence Science and Technology, National University of Defense Technology（智能科学与技术学院，国防科技大学）
Plain Transformers are Surprisingly Powerful Link Predictors
Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA.（计算机科学与工程系，密歇根州立大学，东兰辛，MI，美国。）；Department of Computer Science（计算机科学系）；Engineering, Michigan State University, East Lansing, MI, USA.（工程，密歇根州立大学，东兰辛，MI，美国。）
