השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| מרכזיות וקטור עצמי משוקללת× | מדד קרבה משוקלל× | |
|---|---|---|
| תחום | ניתוח רשתות | ניתוח רשתות |
| משפחה | Machine learning | Machine learning |
| שנת המקור≠ | 1987 (binary); 2010 (weighted generalization) | 2010 |
| הוגה השיטה≠ | Bonacich, P. (binary); Opsahl, T. et al. (weighted extension) | Opsahl, T.; Agneessens, F.; Skvoretz, J. |
| סוג≠ | Spectral centrality measure | Centrality measure (network analysis) |
| מקור מכונן≠ | Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182. DOI ↗ | Opsahl, T., Agneessens, F. & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251. DOI ↗ |
| כינויים | WEC, weighted spectral centrality, strength-weighted eigenvector centrality, weighted eigenvector prestige | weighted closeness, generalized closeness centrality, WCC, distance-weighted closeness |
| קשורות | 6 | 6 |
| תקציר≠ | Weighted eigenvector centrality extends the classic eigenvector centrality measure to graphs where edges carry numerical weights, scoring each node proportionally to the sum of its neighbors' scores multiplied by the connecting edge weights. Nodes score highly not just by having many connections but by being strongly linked to other influential nodes, making the measure sensitive to both tie strength and network position simultaneously. | Weighted closeness centrality extends the classic closeness measure to networks where edges carry numerical weights — such as frequency, strength, or cost — by incorporating those weights into shortest-path distances. Nodes that can reach others quickly along strong or efficient connections receive higher scores, making it a richer indicator of information-spreading potential than its binary counterpart. |
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