방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 가중치 고유벡터 중심성× | 가중치 PageRank× | |
|---|---|---|
| 분야 | 네트워크 분석 | 네트워크 분석 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 1987 (binary); 2010 (weighted generalization) | 2004 |
| 창시자≠ | Bonacich, P. (binary); Opsahl, T. et al. (weighted extension) | Xing, W. & Ghorbani, A. |
| 유형≠ | Spectral centrality measure | Centrality measure / ranking algorithm |
| 원전≠ | Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182. DOI ↗ | Xing, W., & Ghorbani, A. (2004). Weighted PageRank algorithm. Proceedings of the Second Annual Conference on Communication Networks and Services Research (CNSR '04), pp. 305–314. IEEE. DOI ↗ |
| 별칭 | WEC, weighted spectral centrality, strength-weighted eigenvector centrality, weighted eigenvector prestige | WPR, weighted page rank, edge-weighted PageRank, strength-based PageRank |
| 관련 | 6 | 6 |
| 요약≠ | 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 PageRank extends the classic PageRank algorithm to networks where edges carry different strengths or frequencies, distributing importance proportionally to both incoming and outgoing edge weights rather than treating all links equally. This makes it substantially more informative than binary PageRank in any network where connection strength matters. |
| ScholarGate데이터셋 ↗ |
|
|