通过优化反馈边，揭示果蝇神经连接组的前馈结构。
Feedforward Ordering in Neural Connectomes via Feedback Arc Minimization
- 基于强连通分量分析与贪心启发式算法，减少图中反馈边。
- 在FlyWire数据集上，前向边权重提升超过此前最优方法。
- 适合神经科学与复杂网络研究者，可扩展至大规模连接组分析。
我们提出一套可扩展的算法，用于最小化大规模加权有向图中的反馈边，旨在揭示神经连接组中具有生物学意义的前馈结构。利用FlyWire Connectome Challenge数据集，我们验证了排序策略在最大化前向边总权重方面的有效性。方法融合了贪心启发式、感知增益的局部优化以及基于强连通分量的全局结构分析。实验表明，最佳方案在前向边权重上优于此前表现最优的方法。所有算法均以高效方式实现于Python，并在Google Colab Pro+云端环境完成验证。
原文摘要 · Abstract (English)
We present a suite of scalable algorithms for minimizing feedback arcs in large-scale weighted directed graphs, with the goal of revealing biologically meaningful feedforward structure in neural connectomes. Using the FlyWire Connectome Challenge dataset, we demonstrate the effectiveness of our ranking strategies in maximizing the total weight of forward-pointing edges. Our methods integrate greedy heuristics, gain-aware local refinements, and global structural analysis based on strongly connected components. Experiments show that our best solution improves the forward edge weight over previous top-performing methods. All algorithms are implemented efficiently in Python and validated using cloud-based execution on Google Colab Pro+.
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