Social networks often contain dense and overlapping connections that obscure their essential interaction patterns, making analysis and interpretation challenging. Identifying the structural backbone of such networks is crucial for understanding community organization, information flow, and functional relationships. This study introduces a multi-step network pruning framework that leverages principles from information theory to balance structural complexity and task-relevant information. The framework iteratively evaluates and removes edges from the graph based on their contribution to task-relevant mutual information, producing a trajectory of network simplification that preserves most of the inherent semantics. Motivated by gradient boosting, we propose IGPrune, which enables efficient, differentiable optimization to progressively uncover semantically meaningful connections. Extensive experiments on social and biological networks show that IGPrune retains critical structural and functional patterns. Beyond quantitative performance, the pruned networks reveal interpretable backbones, highlighting the method's potential to support scientific discovery and actionable insights in real-world networks.

翻译：社会网络通常包含密集且重叠的连接，这些连接模糊了其本质的交互模式，使得分析和解释变得困难。识别此类网络的结构骨干对于理解社区组织、信息流和功能关系至关重要。本研究引入了一种多步网络剪枝框架，该框架利用信息论原理来平衡结构复杂性与任务相关信息。该框架基于边对任务相关互信息的贡献，迭代地评估并从图中移除边，从而产生一个保留大部分固有语义的网络简化轨迹。受梯度提升的启发，我们提出了IGPrune，它能够通过高效、可微的优化逐步揭示具有语义意义的连接。在社会和生物网络上的大量实验表明，IGPrune保留了关键的结构和功能模式。除了定量性能之外，剪枝后的网络揭示了可解释的骨干结构，突显了该方法在支持现实世界网络中的科学发现和可操作见解方面的潜力。