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| Phân tích Mạng Đa Lớp Định Hướng× | Phát hiện cộng đồng có hướng× | |
|---|---|---|
| Lĩnh vực | Phân tích mạng lưới | Phân tích mạng lưới |
| Họ | Machine learning | Machine learning |
| Năm ra đời≠ | 2013–2014 | 2008 |
| Người khởi xướng≠ | Kivela, M.; De Domenico, M. et al. | Leicht, E. A. & Newman, M. E. J.; Rosvall, M. & Bergstrom, C. T. |
| Loại≠ | Multi-layer directed graph framework | Graph partitioning / modularity optimization |
| Công trình gốc≠ | Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271. DOI ↗ | Leicht, E. A. & Newman, M. E. J. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703. DOI ↗ |
| Tên gọi khác | directed multilayer network analysis, directed multiplex graphs, asymmetric multiplex network analysis, DMNA | directed graph clustering, community detection in digraphs, directed modularity optimization, directed network partitioning |
| Liên quan | 6 | 6 |
| Tóm tắt≠ | Directed multiplex network analysis models systems where the same set of nodes are connected by multiple types of directed (asymmetric) relationships across distinct layers — such as citation flows, information cascades, or authority hierarchies co-existing simultaneously. It extends multiplex network analysis by preserving both layer identity and edge directionality, enabling richer structural and dynamic insights. | Directed community detection identifies densely interconnected groups of nodes in a directed network, accounting for the asymmetry of edges (e.g., A follows B does not imply B follows A). Adapting modularity or flow-based criteria to directed graphs reveals clusters that undirected methods systematically miss, making it essential for citation networks, follower graphs, and biological regulatory pathways. |
| ScholarGateBộ dữ liệu ↗ |
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