Graph Attention Network (GAT) is one of the most popular Graph Neural Network (GNN) architecture, which employs the attention mechanism to learn edge weights and has demonstrated promising performance in various applications. However, since it only incorporates information from immediate neighborhood, it lacks the ability to capture long-range and global graph information, leading to unsatisfactory performance on some datasets, particularly on heterophilic graphs. To address this limitation, we propose the Directional Graph Attention Network (DGAT) in this paper. DGAT is able to combine the feature-based attention with the global directional information extracted from the graph topology. To this end, a new class of Laplacian matrices is proposed which can provably reduce the diffusion distance between nodes. Based on the new Laplacian, topology-guided neighbour pruning and edge adding mechanisms are proposed to remove the noisy and capture the helpful long-range neighborhood information. Besides, a global directional attention is designed to enable a topological-aware information propagation. The superiority of the proposed DGAT over the baseline GAT has also been verified through experiments on real-world benchmarks and synthetic data sets. It also outperforms the state-of-the-art (SOTA) models on 6 out of 7 real-world benchmark datasets.
Single-Pass Contrastive Learning Can Work for Both Homophilic and Heterophilic Graph
National University of Singapore（新加坡国立大学）；University of Washington（华盛顿大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）
Unsupervised Graph Anomaly Detection via Multi-Hypersphere Heterophilic Graph Learning
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Shanghai University（上海大学）
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）
Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation
National University of Singapore（新加坡国立大学）；Zhejiang University（浙江大学）；Alibaba Group（阿里巴巴集团）；Zhejiang Lab（之江实验室）；Hangzhou Yugu Technology Co., Ltd.（杭州玉谷科技有限公司）
RAwR: Role-Aware Rewiring via Approximate Equitable Partition
Department of Excellence L’EMbeDS, Sant’Anna School of Advanced Studies, Pisa, Italy（卓越部门L’EMbeDS，圣安娜高级研究学院，意大利比萨）；Department of Computer Science, University of Pisa, Italy（计算机科学系，意大利比萨大学）；Department of Statistics, The Pennsylvania State University, USA（统计系，美国宾夕法尼亚州立大学）；Huck Institutes of the Life Sciences, The Pennsylvania State University, USA（生命科学学院Huck研究所，美国宾夕法尼亚州立大学）；IMT School for Advanced Studies, Lucca, Italy（IMT高级研究学院，意大利卢卡）；Computational Network Science, RWTH Aachen University, Aachen, Germany（计算网络科学，德国亚琛工业大学，亚琛）；DTU Technical University of Denmark, Lyngby, Denmark（丹麦技术大学DTU，Lyngby，丹麦）
我们提出了一种新的方法来学习节点表示有向图，它保持单独的视图或嵌入空间的两个不同的节点角色引起的方向性的边缘。我们认为，以前的方法要么未能编码的边缘方向性或他们的编码不能概括跨任务。我们简单的\emph{alternating random walk}策略，我们产生的角色特定的顶点邻域和训练节点嵌入在其相应的源/目标角色，同时充分利用有向图的语义。我们还挖掘了有向图评价的局限性，并提出了一个明确的策略，用于评价有向图中的链接预测和图重构。我们进行了广泛的实验，以展示我们的有效性在几个现实世界的数据集上的链接预测，节点分类和图形重建任务。我们表明，我们的方法的嵌入确实是强大的，可推广的，以及在多种任务和图形的表现。我们表明，我们始终优于所有基线的节点分类任务。除了提供我们的方法的理论解释，我们还表明，我们是相当强大的比其他有向图的方法。
Counterfactual Data Augmentation with Denoising Diffusion for Graph Anomaly Detection
School of Computer and Information Engineering, Henan University（河南大学计算机与信息工程学院）；University of Electronic Science and Technology of China（电子科技大学）；Sichuan University（四川大学）；Iowa State University（爱荷华州立大学）
UrbanGraphEmbeddings: Learning and Evaluating Spatially Grounded Multimodal Embeddings for Urban Science
National University of Singapore Department of Architecture Singapore；The Chinese University of Hong Kong Dept of Systems Eng. \& Eng. Mgmt. China；Singapore Management University School of Computing \& Info. Systems Singapore；National University of Singapore；Department of Architecture；The Chinese University of Hong Kong；Singapore Management University
SCNode: Spatial and Contextual Coordinates for Graph Representation Learning
Department of Mathematical Science（数学科学系）；The University of Texas at Dallas（德克萨斯大学达拉斯分校）；Department of Mathematics（数学系）；Florida State University（佛罗里达州立大学）；Department of Computer Science（计算机科学系）；AI Initiative（人工智能计划）；University of Central Florida（中央佛罗里达大学）
SAHG: Sector-Anisotropic Hyperbolic Graph Model for Social Bot Detection
University of Leeds（利兹大学）；University of Science and Technology of China（中国科学技术大学）；South China Normal University（华南师范大学）；Tsinghua University（清华大学）；The Chinese University of Hong Kong（香港中文大学）；Harbin University of Commerce（哈尔滨商业大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Peking University（北京大学）；University of California, Berkeley（加州大学伯克利分校）
Learning Propagation Geometry from Message-Passing Feedback
The Chinese University of Hong Kong（香港中文大学）；The Education University of Hong Kong（香港教育大学）；National University of Defense Technology（国防科技大学）；Hebei University of Technology（河北工业大学）；City University of Hong Kong（香港城市大学）；Huazhong University of Science and Technology（华中科技大学）
