方法对比
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| 多层网络分析× | 中心性分析× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 2013–2014 (formal mathematical framework) | 1979 |
| 提出者≠ | Kivelä et al. (2014); De Domenico et al. (2013) | Linton C. Freeman |
| 类型≠ | Graph-theoretic network model | Descriptive / exploratory network measure family |
| 开创性文献≠ | Kivelä, M. et al. (2014). Multilayer Networks. Journal of Complex Networks, 2(3), 203–271. DOI ↗ | Freeman, L.C. (1979). Centrality in Social Networks: Conceptual Clarification. Social Networks, 1(3), 215-239. DOI ↗ |
| 别名≠ | multiplex network analysis, multiplex networks, Çok Katmanlı Ağ Analizi (Multiplex Networks) | Merkeziyet Analizi (Degree, Betweenness, Eigenvector), node centrality, centrality measures, graph centrality |
| 相关≠ | 6 | 5 |
| 摘要≠ | Multilayer network analysis is a graph-theoretic framework, formalised by Kivelä et al. (2014) and De Domenico et al. (2013), that represents the same set of nodes simultaneously across multiple relationship layers. Where a single-layer network collapses all relationships into one graph, the multilayer model preserves the distinct relational context of each layer — social platform, biological interaction type, or infrastructure tier — while also modelling how layers couple with each other through interlayer edges. | Centrality analysis is a family of network-analytic measures, formalized by Freeman (1979), that quantifies the structural importance of individual nodes within a graph. Each centrality index captures a distinct mechanism of influence: degree centrality reflects direct connectivity, betweenness centrality identifies nodes that broker information flow, closeness centrality captures proximity to all others, and eigenvector centrality (along with PageRank) rewards connection to highly connected neighbors. |
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