قارن الطرق
راجع الطرق التي اخترتها جنبًا إلى جنب؛ الصفوف المختلفة مميَّزة.
| اكتشاف المجتمعات الموزونة× | تحليل الشبكات الاجتماعية× | |
|---|---|---|
| المجال | تحليل الشبكات | تحليل الشبكات |
| العائلة | Machine learning | Machine learning |
| سنة النشأة≠ | 2004–2008 | 1934 (sociometry); 1994 (modern formalization) |
| صاحب الطريقة≠ | Newman, M. E. J.; Blondel et al. | Moreno, J.L.; formalized by Wasserman & Faust |
| النوع≠ | Graph clustering / community detection | Structural/relational analysis framework |
| المصدر التأسيسي≠ | Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. DOI ↗ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 |
| الأسماء البديلة | weighted graph clustering, community detection on weighted networks, weighted modularity optimization, WCD | SNA, network analysis, sociometric analysis, relational analysis |
| ذات صلة≠ | 6 | 5 |
| الملخص≠ | Weighted community detection identifies densely connected groups — communities — in networks where edges carry numeric strengths (weights). By incorporating edge weights into the modularity function, it reveals structure that binary adjacency alone would miss: two nodes connected by a strong tie are treated as more similar than two nodes linked by a weak one. The Louvain algorithm is the dominant practical implementation. | Social Network Analysis (SNA) is a structural method that maps and measures relationships and flows between people, groups, organizations, or other entities modeled as nodes connected by ties (edges). Rather than focusing on individual attributes, SNA reveals how the pattern of connections shapes behavior, influence, information flow, and outcomes within a system. |
| ScholarGateمجموعة البيانات ↗ |
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