arXiv:2005.04806
공식 코드 · Papers with Code
Comparison and Benchmark of Graph Clustering Algorithms
원문 초록
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.
함께 살펴볼 논문
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Tri-Learn Graph Fusion Network for Attributed Graph Clustering
Unsupervised Graph Clustering with Deep Structural Entropy
Adaptive Local Clustering over Attributed Graphs
Imputation-free and Alignment-free: Incomplete Multi-view Clustering Driven by Consensus Semantic Learning
Rhomboid Tiling for Geometric Graph Deep Learning
HAECcity: Open-Vocabulary Scene Understanding of City-Scale Point Clouds with Superpoint Graph Clustering
토론
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