Heterogeneous Graph Neural Networks (HGNNs) have exhibited remarkable efficacy in modeling complex systems with multiple types of nodes and relations, yet their training on large-scale heterogeneous graphs remains computationally prohibitive. Although graph condensation methods can effectively improve learning efficiency on large-scale graphs, existing condensation processes are mainly designed for homogeneous graphs and typically rely on computationally expensive gradient matching or bilevel optimization paradigms, rendering them impractical for heterogeneous settings. To address these limitations, we propose HGC-RC, a simple yet effective role-aware heterogeneous graph condensation framework. Specifically, HGC-RC first extracts semantically enhanced node embeddings via lightweight propagation. It then introduces a role-aware hybrid clustering strategy consisting of class-partitioned clustering for labeled target nodes to preserve class distributions and unsupervised type-wise clustering for non-target nodes to retain critical cross-type connectivity. Finally, a compact heterogeneous graph is efficiently reconstructed based on the resulting cluster assignments. Extensive experiments demonstrate that HGC-RC outperforms state-of-the-art baselines, offering a practical pathway to accelerate HGNN training on large-scale heterogeneous graphs without sacrificing task performance
Federated Incomplete Multi-View Clustering with Heterogeneous Graph Neural Networks
Guangdong University of Foreign Studies（广东外语外贸大学）；School of Engineering, Westlake University（西湖大学工程学院）；East China University of Science and Technology（华东理工大学）
Florida State University（佛罗里达州立大学）；JD Finance America Corporation（京东金融美国公司）
图表示学习是学习既保留节点属性又保留结构信息的通用节点表示。导出的节点表示可以用于服务于各种下游任务，例如节点分类和节点聚类。当一个图是异构的，问题变得更具挑战性比同构图节点学习问题。受新兴的基于信息理论的学习算法的启发，本文提出了一种用于异构图表示学习的无监督图神经网络Heterogeneous Deep Graph Infomax（HDGI）。我们使用元路径结构来分析异构图中涉及语义的连接，并利用图卷积模块和语义级注意力机制来捕获局部表示。通过最大化局部-全局互信息，HDGI有效地学习可以在下游图相关任务中使用的高级节点表示。实验结果表明，HDGI在分类和聚类任务上都明显优于最先进的无监督图表示学习方法。通过将学习到的表示馈送到参数模型（如逻辑回归）中，我们甚至在节点分类任务中实现了与最先进的监督端到端GNN模型相比相当的性能。
Bridge the Gaps: Heterogeneous Attributed Graph Clustering via Quaternion Representation Learning
School of Computer Science and Technology, Guangdong University of Technology（广东工业大学计算机科学与技术学院）；Tsinghua University（清华大学）；BH-Energy Technology Co., Ltd.（BH-Energy科技有限公司）；Shenzhen MSU-BIT University（深圳北理莫斯科大学）
College of Mathematics and Informatics, South China Agricultural University（华南农业大学数学与信息学院）；Key Laboratory of Smart Agricultural Technology in Tropical South China, Ministry of Agriculture and Rural Affairs（农业农村部华南热带智慧农业技术重点实验室）；School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机学院）
Clustering in Dynamic Environments: A Framework for Benchmark Dataset Generation With Heterogeneous Changes
Faculty of Engineering & Information Technology, University of Technology Sydney（悉尼科技大学工程与信息技术学院）；Warwick Business school, University of Warwick（华威大学华威商学院）；School of Computing and Leeds University Business School, University of Leeds（利兹大学计算学院与利兹大学商学院）；RMIT University（皇家墨尔本理工大学）；University Research and Innovation Center (EKIK), Obuda University（奥布达大学大学研究与创新中心）；Lingnan University（岭南大学）；CERCIA, School of Computer Science, University of Birmingham（伯明翰大学计算机科学学院CERCIA研究中心）
A Multi-disciplinary Ensemble Algorithm for Clustering Heterogeneous Datasets
Kurdistan Institution for Strategic Studies and Scientific Research（库尔德斯坦战略研究与科学研究机构）；University of Kurdistan Hewler（库尔德斯坦赫勒大学）；Sulaimani Polytechnic University（苏莱曼尼理工大学）
A One-shot Framework for Distributed Clustered Learning in Heterogeneous Environments
Carnegie Mellon University（卡内基梅隆大学）；Faculty of Technical Sciences, University of Novi Sad（诺维萨德大学技术科学学院）；Faculty of Sciences, University of Novi Sad（诺维萨德大学理学院）
Dual-Segment Clustering Strategy for Hierarchical Federated Learning in Heterogeneous Wireless Environments
College of Electronics and Information Engineering, Tongji University（同济大学电子与信息工程学院）；Tongji Hospital, Tongji University（同济大学附属同济医院）；Data61, CSIRO（澳大利亚联邦科学与工业研究组织Data61研究院）；Guangzhou Huatu Information Technology Co., Ltd.（广州华图信息技术有限公司）；School of Electrical and Computer Engineering, The University of Sydney（悉尼大学电气与计算机工程学院）
Learning Unified Distance Metric for Heterogeneous Attribute Data Clustering
School of Computer Science and Technology（计算机科学与技术学院）；Guangdong University of Technology（广东工业大学）；Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China（教育部多媒体可信感知与高效计算重点实验室）；Xiamen University（厦门大学）；Department of Computer Science（计算机科学系）
Performance evaluation results of evolutionary clustering algorithm star for clustering heterogeneous datasets
Kurdistan Institution for Strategic Studies and Scientific Research（库尔德斯坦战略研究与科学研究所）；University of Kurdistan Hewler（库尔德斯坦赫勒大学）；Sulaimani Polytechnic University（苏莱曼尼亚理工大学）；Torrens University（托伦斯大学）；Yonsei University（延世大学）
Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs
GIPSA-lab（GIPSA实验室）；UGA（格勒诺布尔阿尔卑斯大学）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔国立高等物理、数学与信息学院）；L2S（信号与系统实验室）；CentraleSupélec（中央理工-高等电力学院）；University of Paris Saclay（巴黎萨克雷大学）
A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks
School of Computer Science and Artificial Intelligence, Wuhan University of Technology（计算机科学与人工智能学院，武汉科技大学）；Hubei Key Laboratory of Transportation Internet of Things, Wuhan University of Technology（交通物联网湖北省重点实验室，武汉科技大学）；School of Engineering, Swinburne University of Technology（工程学院，斯威本科技大学）；School of Computer Science and Technology, Tongji University（计算机科学与技术学院，同济大学）；State Key Laboratory for Novel Software Technology, Nanjing University（新型软件技术国家重点实验室，南京大学）
A Unified Framework for Structure-Aware Clustering and Heterogeneous Causal Graph Learning
Department of Industrial and Systems Engineering, University of Florida（佛罗里达大学工业与系统工程系）；Department of Biostatistics, MD Anderson Cancer Center（MD安德森癌症中心生物统计学系）
North China Electric Power University（华北电力大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Beihang University（北京航空航天大学）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）
