方法对比
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| 加权多层网络分析× | 加权社区检测× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2014 | 2004–2008 |
| 提出者≠ | Battiston, F.; Kivela, M. et al. | Newman, M. E. J.; Blondel et al. |
| 类型≠ | Network analysis framework | Graph clustering / community detection |
| 开创性文献≠ | Battiston, F., Nicosia, V., & Latora, V. (2014). Structural measures for multiplex networks. Physical Review E, 89(3), 032804. DOI ↗ | 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 ↗ |
| 别名 | WMNA, weighted multilayer network analysis, weighted multi-relational network analysis, multiplex weighted graph analysis | weighted graph clustering, community detection on weighted networks, weighted modularity optimization, WCD |
| 相关≠ | 5 | 6 |
| 摘要≠ | Weighted multiplex network analysis studies systems in which the same set of actors are connected through multiple types of relationships simultaneously, and each relationship carries a quantitative strength or frequency. By capturing both the variety and the intensity of ties across layers, it reveals patterns invisible to single-layer or unweighted network approaches. | 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. |
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