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[2602.16204] Linked Data Classification using Neurochaos Learning
Summary
This article explores the application of Neurochaos Learning (NL) to linked data classification, demonstrating its effectiveness on knowledge graphs and comparing performance on homophilic and heterophilic datasets.
Why It Matters
The research highlights a novel approach to linked data classification using Neurochaos Learning, which may offer advantages over traditional deep learning methods, especially in scenarios with limited data. This could have significant implications for fields relying on knowledge graphs, such as semantic web technologies and AI-driven data analysis.
Key Takeaways
- Neurochaos Learning shows promise for linked data classification.
- The method is effective with small training samples and low compute requirements.
- Performance varies between homophilic and heterophilic graph datasets.
- Node aggregation enhances the application of NL in knowledge graphs.
- Future research directions are suggested for further exploration.
Computer Science > Machine Learning arXiv:2602.16204 (cs) [Submitted on 18 Feb 2026] Title:Linked Data Classification using Neurochaos Learning Authors:Pooja Honna, Ayush Patravali, Nithin Nagaraj, Nanjangud C. Narendra View a PDF of the paper titled Linked Data Classification using Neurochaos Learning, by Pooja Honna and 3 other authors View PDF HTML (experimental) Abstract: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. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2602.16204 [cs.LG]...
