Research map / Probabilistic and scientific modeling / Dynamical systems
Dependent Relational Gamma Process Models for Longitudinal Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Bernoulli-Poisson link with thinned group dynamics (DRGPM) instead of logistic link (DRIFT) | longitudinal network model · FFDC, DBLP, Enron | seconds per iteration: 0.623 vs 164.342 on FFDC | Helped |
| Dynamic models vs static HGPEPM on DBLP | DRGPM · DBLP | PR (missing link): 0.425 vs 0.791 for HGPEPM | Hurt |
| Replace logistic link (DRIFT) with BPL-based DRGPM | missing link prediction · FFDC | AUROC: 0.924 vs 0.933 for DRIFT | Mixed |
| Gamma Markov chain node-group membership dynamics with group birth/death thinning (DRGPM) vs D-GPPF | missing link prediction · Enron | PR: 0.597 vs 0.499 for D-GPPF | Helped |
| Gamma Markov chain node-group membership dynamics with group birth/death thinning (DRGPM) vs DPGM | future network forecasting · Enron | AUROC: 0.886 vs 0.883 | Helped |
| Explicit group birth/death thinning vs DRGPM-free static membership (D-GPPF) in forecasting | DRGPM · FFDC, DBLP | AUROC (forecasting): 0.852 vs 0.842 (FFDC, D-GPPF); 0.753 vs 0.734 (DBLP, D-GPPF) | Helped |
| Gamma Markov membership dynamics + thinning vs DPGM (no group thinning): per-iteration time | DRGPM · FFDC, DBLP, Enron | seconds per iteration: 0.623 vs 0.748 (FFDC); 1.217 vs 1.676 (DBLP) | Helped |
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