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| Súlyozott Exponenciális Grafmodell (W-ERGM)× | Súlyozott modularitásanalízis× | |
|---|---|---|
| Tudományterület | Hálózatelemzés | Hálózatelemzés |
| Módszercsalád | Machine learning | Machine learning |
| Keletkezés éve≠ | 2012 | 2004 |
| Megalkotó≠ | Krivitsky, P. N. | Newman, M. E. J. |
| Típus≠ | Statistical network model | Community structure optimization on weighted graphs |
| Alapmű≠ | Krivitsky, P. N. (2012). Exponential-family random graph models for valued networks. Electronic Journal of Statistics, 6, 1100–1128. DOI ↗ | Newman, M. E. J. (2004). Analysis of weighted networks. Physical Review E, 70(5), 056131. DOI ↗ |
| Alternatív nevek | W-ERGM, valued ERGM, weighted p-star model, valued exponential random graph model | weighted modularity, weighted Q optimization, weighted network community detection, strength-based modularity |
| Kapcsolódó≠ | 4 | 5 |
| Összefoglaló≠ | The Weighted Exponential Random Graph Model (W-ERGM) extends the classic binary ERGM framework to networks whose edges carry quantitative values — such as frequency of contact, trade volume, or collaboration intensity. It models the entire valued-edge network as a probability distribution defined over all possible weighted graphs, enabling researchers to test whether structural patterns such as reciprocity, transitivity, or degree distribution arise beyond what chance alone would produce. | Weighted modularity analysis extends the classical Newman-Girvan modularity measure to networks where edges carry numeric strengths (frequencies, intensities, costs). By replacing binary adjacency with tie weights, it finds community partitions that reflect how densely interconnected subgroups are relative to what is expected under a weighted null model, yielding more nuanced groupings than unweighted approaches on data where edge strength varies meaningfully. |
| ScholarGateAdatkészlet ↗ |
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