Interaction valence reveals contrasting social networks in dairy cattle
Summary
This paper presents a valence-aware social-network framework using computer vision to analyze interactions in dairy cattle, revealing contrasting affiliative and agonistic network structures that emphasize the importance of interaction valence in social network analysis.
View Cached Full Text
Cached at: 08/21/26, 09:55 AM
# Interaction valence reveals contrasting social networks in dairy cattle Source: [https://arxiv.org/abs/2608.19222](https://arxiv.org/abs/2608.19222) [View PDF](https://arxiv.org/pdf/2608.19222) > Abstract: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\. ## Submission history From: Suresh Neethirajan \[[view email](https://arxiv.org/show-email/2bdc61e8/2608.19222)\] **\[v1\]**Sun, 26 Jul 2026 10:22:49 UTC \(1,783 KB\)
Similar Articles
REGARD: Regional Affective Differences in Large Language Models
This paper introduces REGARD, a study using Valence-Arousal-Dominance profiling to measure affective framing differences across LLMs on post-Soviet entities, revealing that models cluster by emotional intensity and generic-answer rate rather than origin or size.
Negative Before Positive: Asymmetric Valence Processing in Large Language Models
This paper investigates how large language models process emotional valence through mechanistic interpretability. Using activation patching and steering on three open-source LLMs, the authors find that negative valence is localized to early layers while positive valence peaks in mid-to-late layers, and they validate this through topic-controlled flip tests.
HCIG: A Hierarchical Cross-Modal Incongruity Graph Network for Multimodal Sarcasm and Cyberbullying Detection
This paper proposes HCIG, a hierarchical cross-modal incongruity graph network for multimodal sarcasm and cyberbullying detection, achieving state-of-the-art performance on the MMSD and MultiBully datasets.
Nine Emotion Centroids: A Label-Free Valence Axis That Transfers Across Four Modalities
This paper introduces a label-free method to find a valence axis from nine emotion examples that transfers across text, vision, audio, and brain modalities, achieving competitive sentiment classification with minimal labels.
From Descriptive to Prescriptive: Uncover the Social Value Alignment of LLM-based Agents
This paper proposes SoVA, a framework using GraphRAG to align LLM-based agents with human social values by converting psychological theories into prescriptive instructions. Experiments on the DAILYDILEMMAS benchmark show significant improvements over prompt-based baselines.