Vertaile menetelmiä
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| Suunnattu verkkodiffuusion analyysi× | Ohjattu yhteisöntunnistus× | |
|---|---|---|
| Tieteenala | Verkostoanalyysi | Verkostoanalyysi |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2003 (influence maximization formalization); epidemic models traced to Kermack & McKendrick, 1927 | 2008 |
| Kehittäjä≠ | Kempe, D.; Kleinberg, J.; Tardos, E. (influence maximization); Pastor-Satorras, R. et al. (epidemic spreading) | Leicht, E. A. & Newman, M. E. J.; Rosvall, M. & Bergstrom, C. T. |
| Tyyppi≠ | Network spreading and cascade analysis | Graph partitioning / modularity optimization |
| Alkuperäislähde≠ | Kempe, D., Kleinberg, J., & Tardos, E. (2003). Maximizing the spread of influence through a social network. Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 137–146. DOI ↗ | Leicht, E. A. & Newman, M. E. J. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703. DOI ↗ |
| Rinnakkaisnimet | directed diffusion model, information spreading on directed networks, directed cascade analysis, directed influence propagation | directed graph clustering, community detection in digraphs, directed modularity optimization, directed network partitioning |
| Liittyvät | 6 | 6 |
| Tiivistelmä≠ | Directed network diffusion analysis studies how information, disease, behavior, or influence spreads through a network in which edges carry direction — meaning transmission flows one way along each link. It combines graph-theoretic representations with stochastic spreading models such as independent cascade, linear threshold, or SIR/SIS, and is central to influence maximization, epidemic forecasting, and information propagation research. | 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. |
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