পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| ওয়েটেড স্টোকাস্টিক ব্লক মডেল× | মডিউলারিটি বিশ্লেষণ× | |
|---|---|---|
| ক্ষেত্র | নেটওয়ার্ক বিশ্লেষণ | নেটওয়ার্ক বিশ্লেষণ |
| পরিবার | Machine learning | Machine learning |
| উদ্ভবের বছর≠ | 2014 | 2004 |
| প্রবর্তক≠ | Aicher, C.; Jacobs, A. Z.; Clauset, A. | Newman, M. E. J. & Girvan, M. |
| ধরন≠ | Generative probabilistic model | Community detection / graph partitioning |
| মৌলিক উৎস≠ | Aicher, C., Jacobs, A. Z., & Clauset, A. (2014). Learning latent block structure in weighted networks. Journal of Complex Networks, 3(2), 221–248. DOI ↗ | Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. DOI ↗ |
| অপর নাম | W-SBM, weighted SBM, weighted block model, weighted community detection via SBM | Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity |
| সম্পর্কিত≠ | 6 | 5 |
| সারসংক্ষেপ≠ | The Weighted Stochastic Block Model (W-SBM) extends the classical stochastic block model to networks whose edges carry numerical weights. By positing that edge weights between node pairs arise from distributions that depend on the block memberships of those nodes, it simultaneously infers a partition of nodes into communities and a set of block-to-block weight parameters — recovering structure invisible to unweighted methods. | 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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