利用节点状态与结构耦合关系推断网络演化顺序
Inferring Network Evolutionary History via Structure-State Coupled Learning
- 通过结构与稳态耦合建模，捕捉拓扑对节点状态的影响
- 在6个真实网络上平均提升4.0%边形成优先级准确率
- 适用于拓扑信息不足的场景，适合网络演化研究者
从仅有单次最终快照且时间标注有限的情况下推断网络演化历史是基础但极具挑战性的问题。现有方法多依赖拓扑结构，常因信息不足和噪声导致效果不佳。本文引入网络稳态动力学——在特定动力学过程下收敛的节点状态——作为额外且广泛可得的观测信号。提出CS$^2$模型，显式建模结构与状态的耦合关系，揭示拓扑如何调控稳态，并联合两者提升边形成顺序的判别能力。在六个真实时序网络上，多种动力学过程下的实验表明，CS$^2$持续优于强基线，平均提升配对边优先级准确率4.0%，全局排序一致性（Spearman-ρ）提升7.7%。同时更准确还原聚类形成、度异质性和枢纽增长等宏观演化轨迹。此外，仅使用稳态的变体在拓扑不可靠时仍具竞争力，凸显稳态作为独立演化推断信号的价值。
原文摘要 · Abstract (English)
Inferring a network's evolutionary history from a single final snapshot with limited temporal annotations is fundamental yet challenging. Existing approaches predominantly rely on topology alone, which often provides insufficient and noisy cues. This paper leverages network steady-state dynamics -- converged node states under a given dynamical process -- as an additional and widely accessible observation for network evolution history inference. We propose CS$^2$, which explicitly models structure-state coupling to capture how topology modulates steady states and how the two signals jointly improve edge discrimination for formation-order recovery. Experiments on six real temporal networks, evaluated under multiple dynamical processes, show that CS$^2$ consistently outperforms strong baselines, improving pairwise edge precedence accuracy by 4.0% on average and global ordering consistency (Spearman-$ρ$) by 7.7% on average. CS$^2$ also more faithfully recovers macroscopic evolution trajectories such as clustering formation, degree heterogeneity, and hub growth. Moreover, a steady-state-only variant remains competitive when reliable topology is limited, highlighting steady states as an independent signal for evolution inference.
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