通过视频分析奶牛互动情感倾向，揭示了群体中亲善与冲突网络的差异。
Interaction valence reveals contrasting social networks in dairy cattle
- 基于姿态识别的视觉算法自动分类奶牛互动为亲善与冲突行为
- 72.4%的互动为冲突类，持续时间占76.0%，网络密度达0.281
- 区分情感极性后发现两种网络结构完全不同，适合动物福利研究
社交关系影响资源获取、冲突暴露与群体稳定，但现有自动化监测常将行为视为孤立事件。本文提出一种情感感知的社会网络框架，将视频捕捉的互动转化为群体层面的亲善与攻击性组织表征。基于一商业牧场挤奶前区域连续7小时39分钟视频，采用姿态驱动的计算机视觉流程分析，经质量控制后保留1,183次候选互动，涉及36头奶牛和177对组合。在198段检测片段的平衡类别审计中，自动标注与人工标注一致率达82.8%，宏F1为0.872（未加权）。聚合网络连通性良好（密度=0.281；传递性=0.513；平均路径长度=1.88），亲善事件形成五个算法社区（模块度Q=0.429）。在观测区域内，预测的攻击性互动占保留事件的72.4%，持续时间占比76.0%。拥有最多伙伴的个体并非中心性最高的个体。按情感极性分离后，亲善与攻击网络呈现显著不同的边集、社区划分及个体位置。因此，合并互动数量会掩盖网络的行为构成。情感感知分析为竞争、亲善与福利相关变化提供可检验框架，但需长期验证后方可用于福利或健康指标。
原文摘要 · Abstract (English)
Social relationships shape access to resources, exposure to conflict and group stability, yet automated livestock monitoring typically treats behaviour as isolated events. Here, we present a valence-aware social-network framework that transforms video-derived interactions into herd-level representations of affiliative and agonistic organization. A pose-based computer-vision pipeline analysed 7 h 39 min of continuous video from the pre-milking area of one commercial dairy farm. After quality control, 1,183 of 1,414 candidate interactions remained, involving 36 cows and 177 dyads. In a predicted-class-balanced audit of 198 pipeline-detected clips, automated and manual labels agreed in 82.8% of cases, with an unweighted audit-sample macro-F1 of 0.872. These values describe the audited sample rather than prevalence-weighted or end-to-end deployment performance. The aggregated network was connected (density = 0.281; transitivity = 0.513; mean path length = 1.88), and predicted affiliative events formed five algorithmic communities (modularity Q = 0.429). Within the observed zone, predicted agonistic interactions comprised 72.4% of retained events and 76.0% of interaction duration. The cow with the most partners did not have the highest betweenness centrality. Separating events by predicted valence produced descriptively different affiliative and agonistic layers, with contrasting edge sets, community partitions and individual positions. Thus, pooled interaction counts can obscure the behavioural composition of an observed network. Valence-aware analysis provides a framework for testing hypotheses about competition, affiliation and welfare-relevant change, while requiring longitudinal validation before use as a welfare or health indicator.
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