השוואת שיטות
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| מודל גרף אקראי אקספוננציאלי דינמי× | ניתוח רשתות זמניות× | |
|---|---|---|
| תחום | ניתוח רשתות | ניתוח רשתות |
| משפחה≠ | Machine learning | Process / pipeline |
| שנת המקור≠ | 2010–2014 | 2012 |
| הוגה השיטה≠ | Hanneke, Fu & Xing; Krivitsky & Handcock | Holme & Saramäki (2012) — seminal framework |
| סוג≠ | Probabilistic graphical model (temporal) | Dynamic graph analysis |
| מקור מכונן≠ | Hanneke, S., Fu, W., & Xing, E. P. (2010). Discrete temporal models of social networks. Electronic Journal of Statistics, 4, 585–605. DOI ↗ | Holme, P. & Saramäki, J. (2012). Temporal Networks. Physics Reports, 519(3), 97-125. DOI ↗ |
| כינויים≠ | TERGM, Temporal ERGM, Dynamic ERGM, STERGM | dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks) |
| קשורות≠ | 4 | 3 |
| תקציר≠ | The Dynamic Exponential Random Graph Model (TERGM / STERGM) extends the classic ERGM framework to panel network data, modeling how a network's ties form and dissolve over time as a function of structural tendencies, nodal attributes, and the network's own past state. It provides statistically principled inference about longitudinal network change. | Temporal network analysis, formalised by Holme and Saramäki in their landmark 2012 Physics Reports survey, is the study of networks in which edges appear and disappear over time. Rather than collapsing all contacts into a single static graph, the approach preserves the precise timing of interactions — whether as contact sequences, time-stamped event lists, or windowed snapshots — and uses that timing to track how influence, disease, or information can actually propagate through the system. |
| ScholarGateמערך נתונים ↗ |
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