Graph clustering is widely used in analysis of biological networks, social networks and etc. For over a decade many graph clustering algorithms have been published, however a comprehensive and consistent performance comparison is not available. In this paper we benchmarked more than 70 graph clustering programs to evaluate their runtime and quality performance for both weighted and unweighted graphs. We also analyzed the characteristics of ground truth that affects the performance. Our work is capable to not only supply a start point for engineers to select clustering algorithms but also could provide a viewpoint for researchers to design new algorithms.
CSTS: A Benchmark for the Discovery of Correlation Structures in Time Series Clustering
School of Computer Science, University of Bristol（计算机科学学院，布里斯托大学）；School of Engineering Mathematics and Technology, University of Bristol（工程数学与技术学院，布里斯托大学）；School of Artificial Intelligence, University of Nanjing（人工智能学院，南京大学）；College of Engineering and Architecture, University College Dublin（工程与建筑学院，都柏林大学学院）
Benchmarking of Clustering Validity Measures Revisited
School of Information and Physical Sciences, The University of Newcastle（信息与物理科学学院，新castle大学）；Department of Mathematics and Computer Science, University of Southern Denmark（数学与计算机科学系，南部丹麦大学）
Clustering Malware at Scale: A First Full-Benchmark Study
Faculty of Information Technology, Brno University of Technology, Brno, Czechia（布拉格技术学院，布拉格技术大学）；Kempelen Institute of Intelligent Technologies, Bratislava, Slovakia（智能技术 Kempelen 研究院，布拉迪斯拉发，斯洛伐克）；Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Bratislava, Slovakia（电气工程与信息科技学院，斯洛伐克技术大学，布拉迪斯拉发，斯洛伐克）；Swiss Re（瑞士再保险）
Deep Temporal Graph Clustering: A Comprehensive Benchmark and Datasets
College of Computer Science and Technology, National University of Defense Technology（计算机科学与技术学院，国防科技大学）；National University of Defense Technology（国防科技大学）；Intelligent Game and Decision Lab（智能游戏与决策实验室）；College of Systems Engineering, National University of Defense Technology（系统工程学院，国防科技大学）；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）
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）
