Message-passing Graph Neural Networks (GNNs) are often criticized for their limited expressiveness, issues like over-smoothing and over-squashing, and challenges in capturing long-range dependencies. Conversely, Graph Transformers (GTs) are regarded as superior due to their employment of global attention mechanisms, which potentially mitigate these challenges. Literature frequently suggests that GTs outperform GNNs in graph-level tasks, especially for graph classification and regression on small molecular graphs. In this study, we explore the untapped potential of GNNs through an enhanced framework, GNN+, which integrates six widely used techniques: edge feature integration, normalization, dropout, residual connections, feed-forward networks, and positional encoding, to effectively tackle graph-level tasks. We conduct a systematic re-evaluation of three classic GNNs (GCN, GIN, and GatedGCN) enhanced by the GNN+ framework across 14 well-known graph-level datasets. Our results reveal that, contrary to prevailing beliefs, these classic GNNs consistently match or surpass the performance of GTs, securing top-three rankings across all datasets and achieving first place in eight. Furthermore, they demonstrate greater efficiency, running several times faster than GTs on many datasets. This highlights the potential of simple GNN architectures, challenging the notion that complex mechanisms in GTs are essential for superior graph-level performance. Our source code is available at https://github.com/LUOyk1999/GNNPlus.
Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective
The Chinese University of Hong Kong, Shenzhen, China（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Shenzhen International Center for Industrial and Applied Mathematics（深圳国际工业与应用数学中心）
Full-Spectrum Graph Neural Networks: Expressive and Scalable
Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore（数学科学学院，物理与数学科学学院，南洋理工大学，新加坡637371，新加坡）
Making Classic GNNs Strong Baselines Across Varying Homophily: A Smoothness-Generalization Perspective
Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, Zhejiang University（浙江可及感知与智能系统重点实验室，浙江大学）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Software Technology, Zhejiang University（浙江大学软件学院）；China University of Mining and Technology（中国矿业大学）；Department of Computer Science, New York University Shanghai（纽约大学上海分校计算机科学系）
On Improving Graph Neural Networks for QSAR by Pre-training on Extended-Connectivity Fingerprints
Department of Statistics, University of Oxford（牛津大学统计系）；Mathematical Institute, University of Oxford（牛津大学数学院）；Molecular Informatics and AI, Lhasa Limited（Lhasa有限公司分子信息学与人工智能部门）
Theoretical guarantees for the advantage of GNNs over NNs in generalizing bandlimited functions on Euclidean cubes
Faculty of Mathematics, University of Vienna（维也纳大学数学学院）；Department of Mathematics, Michigan State University（密歇根州立大学数学系）；Department of Statistics and Probability, Michigan State University（密歇根州立大学统计与概率系）
A Critical Audit of Spatiotemporal Forecasting Benchmark Datasets and Models
Imperial College London（帝国理工学院）；Ruhr University Bochum（鲁尔大学波鸿分校）；Queen Mary University of London（伦敦玛丽女王大学）；Ben-Gurion University of the Negev（内盖夫本-古里安大学）
Benchmarking Graph Neural Networks in Solving Hard Constraint Satisfaction Problems
Department of Computing Sciences, Bocconi University（博科尼大学计算机科学系）；Bocconi Institute for Data Science and Analytics (BIDSA)（博科尼数据科学与分析研究所）；Department of Physics and Astronomy and INFN, University of Florence（佛罗伦萨大学物理与天文学系及意大利国家核物理研究所）；Dipartimento di Fisica, Sapienza Università di Roma（罗马萨皮恩扎大学物理系）；CNR - Nanotec, unità di Roma, P.le Aldo Moro 5, 00185 Rome, Italy（意大利罗马国家科研委员会纳米技术单元，Aldo Moro大道5号，00185罗马，意大利）；Department of Theoretical Physics, Physics Faculty, University of Havana（古巴哈瓦那大学理论物理系）；INFN, sezione di Roma1, P.le Aldo Moro 5, 00185 Rome, Italy（意大利罗马1研究所，Aldo Moro大道5号，00185罗马，意大利）
Rethinking Graph Neural Networks for the Graph Coloring Problem
Carnegie Mellon University（卡内基梅隆大学）；The Chinese University of Hong Kong（香港中文大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The University of Texas at Austin（德克萨斯大学奥斯汀分校）
Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks
Mississippi State University（密西西比州立大学）；Virginia Tech（弗吉尼亚理工大学）；South Dakota State University（南达科他州立大学）；Emory University（埃默里大学）；The University of Texas at Dallas（德克萨斯大学达拉斯分校）；JD.COM Silicon Valley Research Center（京东硅谷研究中心）；IBM T. J. Watson Research Center（IBM托马斯·J·沃森研究中心）
When GNNs meet symmetry in ILPs: an orbit-based feature augmentation approach
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）理工学院）；Shenzhen International Center for Industrial and Applied Mathematics（深圳国际工业与应用数学中心）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；School of Artificial Intelligence, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）人工智能学院）；School of Software Engineering, Tongji University, Shanghai（同济大学软件学院）
Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook
Lyles School of Civil and Construction Engineering, Purdue University（普渡大学莱尔土木与建筑工程学院）；Institute for AI Industry Research, Tsinghua University（清华大学人工智能产业研究院）
Decision-focused Graph Neural Networks for Combinatorial Optimization
Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；Guangzhou University（广州大学）；Griffith University（格里菲斯大学）；Hangzhou Yugu Technology（杭州宇谷科技）；Zhejiang Lab（之江实验室）
Curriculum-Enhanced Residual Soft An-Isotropic Normalization for Over-smoothness in Deep GNNs
Fuzhou University（福州大学）；HKUST (Guangzhou)（香港科技大学（广州））；Shandong Provincial Key Laboratory of Computer Networks（山东省计算机网络重点实验室）；Shandong Fundamental Research Center for Computer Science（山东计算机科学基础研究中心）
ENADPool: The Edge-Node Attention-based Differentiable Pooling for Graph Neural Networks
School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；School of Information, Central University of Finance and Economics（中央财经大学信息学院）；Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University（浙江师范大学浙江省智能教育技术与应用重点实验室）；Department of Computer Science, University of York（约克大学计算机科学系）
Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing
Nara Institute of Science and Technology（奈良先端科学技术大学院大学）；Kyoto University（京都大学）；Ateneo de Manila University（马尼拉雅典耀大学）；UNI-President Information Philippines Corporation（统一信息菲律宾公司）；The Chinese University of Hong Kong（香港中文大学）
University of Chinese Academy of Sciences（中国科学院大学）；University of Rochester（罗切斯特大学）；University of Science and Technology of China（中国科学技术大学）；Peng Cheng Laboratory（鹏城实验室）
Decouple Graph Neural Networks: Train Multiple Simple GNNs Simultaneously Instead of One
School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University（西北工业大学光电与人工智能学院）；Institute of Artificial Intelligence (TeleAI), China Telecom Corp Ltd（中国电信股份有限公司人工智能研究院）
