विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| भारित द्वि-मोड नेटवर्क विश्लेषण× | मॉड्यूलरिटी विश्लेषण× | |
|---|---|---|
| क्षेत्र | नेटवर्क विश्लेषण | नेटवर्क विश्लेषण |
| परिवार | Machine learning | Machine learning |
| उद्भव वर्ष≠ | 1997 (two-mode); weighted extensions 2000s | 2004 |
| प्रवर्तक≠ | Borgatti, S. P. & Everett, M. G. | Newman, M. E. J. & Girvan, M. |
| प्रकार≠ | Network structural analysis | Community detection / graph partitioning |
| मौलिक स्रोत≠ | Borgatti, S. P., & Everett, M. G. (1997). Network analysis of 2-mode data. Social Networks, 19(3), 243–269. DOI ↗ | Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. DOI ↗ |
| उपनाम | weighted bipartite network analysis, valued two-mode network analysis, weighted affiliation network analysis, W2MNA | Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity |
| संबंधित≠ | 6 | 5 |
| सारांश≠ | Weighted two-mode network analysis examines bipartite graphs in which two distinct node sets — such as actors and events, authors and papers, or species and habitats — are connected by edges carrying numerical weights that capture the strength, frequency, or intensity of each affiliation. Incorporating weights provides substantially richer structural insights than unweighted bipartite analysis. | 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. |
| ScholarGateडेटासेट ↗ |
|
|