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| Phân tích mạng đa lớp× | Phát hiện Cộng đồng× | |
|---|---|---|
| Lĩnh vực | Phân tích mạng lưới | Phân tích mạng lưới |
| Họ | Process / pipeline | Process / pipeline |
| Năm ra đời≠ | 2013–2014 (formal mathematical framework) | 2002–2019 (algorithm family) |
| Người khởi xướng≠ | Kivelä et al. (2014); De Domenico et al. (2013) | Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008) |
| Loại≠ | Graph-theoretic network model | Graph-partitioning / clustering algorithm family |
| Công trình gốc≠ | Kivelä, M. et al. (2014). Multilayer Networks. Journal of Complex Networks, 2(3), 203–271. DOI ↗ | Blondel, V.D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. (2008). Fast Unfolding of Communities in Large Networks. Journal of Statistical Mechanics, 2008(10), P10008. DOI ↗ |
| Tên gọi khác | multiplex network analysis, multiplex networks, Çok Katmanlı Ağ Analizi (Multiplex Networks) | graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden) |
| Liên quan≠ | 6 | 5 |
| Tóm tắt≠ | 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. | Community detection is a family of graph-partitioning algorithms that discover densely connected sub-groups — communities — within a network. First formalised through the modularity measure by Girvan and Newman (2002), the field advanced rapidly with the Louvain method (Blondel et al., 2008), the Leiden refinement (Traag et al., 2019), and the information-theoretic Infomap approach. All variants answer the same question: which nodes cluster together more tightly among themselves than with the rest of the network? |
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