Inferring Network Evolutionary History via Structure-State Coupled Learning

📝 Original Info

• Title: Inferring Network Evolutionary History via Structure-State Coupled Learning
• ArXiv ID: 2601.02121
• Date: 2026-01-05
• Authors: En Xu, Shihe Zhou, Huandong Wang, Jingtao Ding, Yong Li

📝 Abstract

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 dynamicsconverged 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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