Junwen Yu | Searcharxiv

arXiv2026

tSymPerturb converts longitudinal symptom networks into time-indexed intervention strategies

Longitudinal symptom networks encode directed prediction across measurement occasions, but outgoing connectivity does not by itself identify which symptom should be modified, how strongly it should be changed, or how a perturbation would propagate to later symptoms. We introduce tSymPerturb, a temporal extension of SymPerturb for cross-lagged panel networks (CLPNs). The framework separates source-state operators (temporal virtual knockout and knockdown), transition operators (directed edge and source-node communication blocking), and strategy procedures (dosage perturbation, combination analysis and sequence optimisation). For a two-wave linear CLPN, the central propagation identity is $\Delta \mu_2 = B(\mu_1 - \mu_1^*)$, which makes the source time, outcome time and transition operator explicit. The formulation also yields three falsification constraints: dose response is exactly linear under a fixed linear transition model and linear dose map; independent source-state perturbations are additive at the mean level; and genuine treatment order is not identified from a single two-wave transition. In a known 22-node, four-module generating system, analytical temporal-knockout responses agreed with 250,000-draw Monte Carlo estimates within 0.0057 standard deviations. Across 200 independently generated datasets, median Spearman correlation with the population tVPPS ranking increased from 0.76 at n=250 to 0.88 at n=500 and 0.93 at n=1,000; median top-five recovery was 0.60, 0.80 and 0.80, respectively. Multi-wave simulations showed that target profiles can change across propagation horizons despite high overall rank concordance. tSymPerturb therefore converts longitudinal network structure into auditable, time-indexed intervention hypotheses while retaining the distinction between prediction and causal treatment effects.

q-bio.QM↗

arXiv2026

SymPerturb converts symptom-network structure into testable intervention priorities

Symptom networks encode conditional dependence but do not by themselves identify causal or clinically actionable intervention targets. We introduce SymPerturb, a virtual-perturbation framework that distinguishes four primitive perturbation operators - virtual knockout, virtual knockdown, edge-level communication blocking and node-centred communication blocking - from three analytic procedures - virtual dosage perturbation, combination perturbation and sequence optimisation. The reference Gaussian implementation is embedded in a general location-scale map with symptom-specific target anchors, making explicit that zero anchoring and linked mean-variance attenuation are modelling choices. Seven utility outcomes quantify downstream efficacy, dose efficiency, breadth, cross-module reach, communication blocking, combination value and responsiveness; robustness is reported separately as an uncertainty diagnostic. Their direction-aligned, within-candidate-set weighted mean defines the virtual perturbation priority score (VPPS), which is a relative ranking rather than a transportable clinical utility score. In a known 22-node, four-module generating network, analytical efficacy agreed with 100,000-draw Monte Carlo estimates within 0.0024 standard deviations. The reported finite-sample VPPS results were generated with the original eight-component exploratory score and therefore require regeneration under the revised seven-utility-dimension definition. These simulations provide internal computational verification under model compatibility, not causal or external validation. SymPerturb is intended to generate auditable target hypotheses for longitudinal and experimental testing.

q-bio.QM↗