通过优化权重融合不同时段的灵长类社交关系，发现稳定群体结构。
Learning to Fuse Temporal Proximity Networks: A Case Study in Chimpanzee Social Interactions
- 设计新损失函数，按时间连续性自动学习不同距离关系的权重。
- 在合成数据上验证方法有效，能准确恢复已知群体结构。
- 应用于黑猩猩数据，发现可被专家验证的稳定社交小团体。
如何识别可能驱动灵长类社会结构的个体群体？为此，研究者收集了黑猩猩社交互动的时间序列数据。本文采用网络表示法，将多时段数据整合为每个时间戳对应的加权网络，不同接近度赋予不同权重以反映其重要性。通过创新的损失函数，在保证相邻时间步结构一致性前提下优化权重。方法在精心设计的合成数据上得到验证。结合统计检验，提出识别长期关联个体群体的方案。应用于真实黑猩猩数据，检测到社交网络时间序列中的团（clique），结果与既有研究和专家观察相符，具备现实合理性。
原文摘要 · Abstract (English)
How can we identify groups of primate individuals which could be conjectured to drive social structure? To address this question, one of us has collected a time series of data for social interactions between chimpanzees. Here we use a network representation, leading to the task of combining these data into a time series of a single weighted network per time stamp, where different proximities should be given different weights reflecting their relative importance. We optimize these proximity-type weights in a principled way, using an innovative loss function which rewards structural consistency for consecutive time steps. The approach is empirically validated by carefully designed synthetic data. Using statistical tests, we provide a way of identifying groups of individuals that stay related for a significant length of time. Applying the approach to the chimpanzee data set, we detect cliques in the animal social network time series, which can be validated by real-world intuition from prior research and qualitative observations by chimpanzee experts.
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