Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Detecção de Comunidades× | Modelo de Grafos Aleatórios Exponenciais (ERGM / p*)× | |
|---|---|---|
| Área | Análise de redes | Análise de redes |
| Família | Process / pipeline | Process / pipeline |
| Ano de origem≠ | 2002–2019 (algorithm family) | 1986 (foundational); modern ERGM framework 1996–2007 |
| Autor original≠ | Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008) | Frank & Strauss (1986); extended by Wasserman & Pattison (1996) and Robins et al. (2007) |
| Tipo≠ | Graph-partitioning / clustering algorithm family | Probabilistic generative network model |
| Fonte seminal≠ | 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 ↗ | Robins, G., Pattison, P., Kalish, Y., & Lusher, D. (2007). An introduction to exponential random graph (p*) models for social networks. Social Networks, 29(2), 173-191. DOI ↗ |
| Outros nomes≠ | graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden) | ERGM, p-star model, p* model, Üstel Rastgele Graf Modeli (ERGM / p*) |
| Relacionados≠ | 5 | 6 |
| Resumo≠ | 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? | The Exponential Random Graph Model (ERGM), also known as the p* model, is a statistical framework for network analysis that models the probability of an observed network as a function of its local structural features — such as reciprocity, triangles, and degree distribution. Developed from the foundational work of Frank and Strauss (1986) and extended into the modern framework by Wasserman and Pattison (1996) and Robins et al. (2007), ERGM is the inferential standard for social network analysis, capable of testing whether observed network structures arise by chance or reflect genuine social processes. |
| ScholarGateConjunto de dados ↗ |
|
|