পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| সমকালীন্য সম্প্রদায় সনাক্তকরণ× | মডিউলারিটি বিশ্লেষণ× | |
|---|---|---|
| ক্ষেত্র | নেটওয়ার্ক বিশ্লেষণ | নেটওয়ার্ক বিশ্লেষণ |
| পরিবার | Machine learning | Machine learning |
| উদ্ভবের বছর≠ | 2010 | 2004 |
| প্রবর্তক≠ | Mucha, P. J. et al. | Newman, M. E. J. & Girvan, M. |
| ধরন≠ | Network clustering algorithm | Community detection / graph partitioning |
| মৌলিক উৎস≠ | Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–878. DOI ↗ | Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. DOI ↗ |
| অপর নাম | dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection | Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity |
| সম্পর্কিত≠ | 6 | 5 |
| সারসংক্ষেপ≠ | Temporal community detection identifies cohesive groups (communities) in networks whose structure changes over time. By treating each time snapshot as a network layer and coupling consecutive layers, it reveals how communities form, merge, split, grow, or dissolve — turning a sequence of static snapshots into a continuous narrative of group evolution. | Modularity analysis is a network science method, formalized by Newman and Girvan in 2004, that detects community structure in graphs by measuring whether edges are more concentrated within groups than expected by chance. Its scalar quality index Q guides algorithms that partition nodes into cohesive clusters, making it the most widely adopted framework for community detection in social, biological, and technological networks. |
| ScholarGateডেটাসেট ↗ |
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