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Real-world interaction networks are inherently dynamic: edges form and dissolve as node behavior shifts over time. Most snapshot-based contrastive methods encode temporal dependencies implicitly in encoder weights, without an explicit model of how node representations evolve, making them brittle under distribution shif...
This study re-run two recent methods, DEMO and NSReg, together with OUTPOST, a small first-order detector built for this study, and finds that a 0.002 tie band for hyperparameter selection lies below the paired standard error on all six graphs tested, even at ten seeds.
QUEST, which adds no trainable parameters to the standard confidence-distribution learning pipeline, improves confidence prediction and link prediction on six of eight metric-dataset pairs over prior methods and matches the previous best on the remaining two, while removing the instability spike observed on dense graph...
RINSE (Robust Iterative Normality Self-Estimation), a gradient-free target-time framework that keeps the source-trained detector fixed while sequentially estimating target normality, representation calibration, and evidence reliability from the target graph, is introduced.
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