TY - JOUR A1 - Mondal, Rahul A1 - Ignatova, Evelina A1 - Walke, Daniel A1 - Broneske, David A1 - Saake, Gunter A1 - Heyer, Robert T1 - Clustering graph data: the roadmap to spectral techniques JF - Discover Artificial Intelligence N2 - AbstractGraph data models enable efficient storage, visualization, and analysis of highly interlinked data, by providing the benefits of horizontal scalability and high query performance. Clustering techniques, such as K-means, hierarchical clustering, are highly beneficial tools in data mining and machine learning to find meaningful similarities and differences between data points. Recent developments in graph data models, as well as clustering algorithms for graph data, have shown promising results in image segmentation, gene data analysis, etc. This has been primarily achieved through research and development of algorithms in the field of spectral theory, leading to the conception of spectral clustering algorithms. Spectral clustering algorithms have been one of the most effective in grouping similar data points in graph data models. In this paper, we have compiled 16 spectral clustering algorithms and compared their computational complexities, after an overview of graph data models and graph database models. Furthermore, we provided a broad taxonomy to classify most existing clustering algorithms and discussed the taxonomy in detail. UR - https://doi.org/10.1007/s44163-024-00102-x Y1 - 2024 UR - https://doi.org/10.1007/s44163-024-00102-x UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-45082 SN - 2731-0809 VL - 4 IS - 1 PB - Springer International Publishing CY - Cham ER - TY - CHAP A1 - Mondal, Rahul A1 - Ignatova, Evelina A1 - Heinzmann, Jonas A1 - Do, Minh Dung A1 - Murali, Abhivanth A1 - Walke, Daniel A1 - Cato, Patrick A1 - Becker, Robert A. A1 - Bleistein, Thomas A1 - Saake, Gunter A1 - Broneske, David A1 - Heyer, Robert T1 - SimKit: Similarity Graphs, Eigendecomposition and Spectral Clustering in Neo4j T2 - Proceedings: 2025 IEEE International Conference on High Performance Computing and Communications, HPCC/DSS/SmartCity/DependSys/ICESS 2025 UR - https://doi.org/10.1109/HPCC67675.2025.00145 Y1 - 2025 UR - https://doi.org/10.1109/HPCC67675.2025.00145 SN - 979-8-3315-6874-0 SP - 985 EP - 991 PB - IEEE CY - Piscataway ER -