用神经混沌学习处理知识图谱分类，小样本下表现更优。
Linked Data Classification using Neurochaos Learning
- 将节点聚合引入知识图谱，输入最简混沌网络进行分类
- 在同质图上效果优于异质图，小样本下性能突出
- 适合低算力场景，尤其适合小样本知识图谱任务
神经混沌学习（Neurochaos Learning, NL）近年来展现出超越传统深度学习的潜力，其两大优势在于能从小样本中学习且计算需求低。此前研究已将其应用于可分数据与时间序列数据，在分类和回归任务中均表现优异。本文探索NL在链接数据中的应用，特别是以知识图谱形式表示的数据。通过在知识图谱上实施节点聚合，并将聚合后的特征输入最简NL架构——ChaosNet，我们验证了该方法在同质图与不同异质性程度的异质图数据集上的表现。结果显示，该方法在同质图上效果优于异质图。同时，我们分析了实验结果并提出未来改进方向。
原文摘要 · Abstract (English)
Neurochaos Learning (NL) has shown promise in recent times over traditional deep learning due to its two key features: ability to learn from small sized training samples, and low compute requirements. In prior work, NL has been implemented and extensively tested on separable and time series data, and demonstrated its superior performance on both classification and regression tasks. In this paper, we investigate the next step in NL, viz., applying NL to linked data, in particular, data that is represented in the form of knowledge graphs. We integrate linked data into NL by implementing node aggregation on knowledge graphs, and then feeding the aggregated node features to the simplest NL architecture: ChaosNet. We demonstrate the results of our implementation on homophilic graph datasets as well as heterophilic graph datasets of verying heterophily. We show better efficacy of our approach on homophilic graphs than on heterophilic graphs. While doing so, we also present our analysis of the results, as well as suggestions for future work.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。