Hyperbolic Graph Embedding for Information-Centric Routing and Content Distribution
Main Article Content
Abstract
The rapid evolution of internet architectures has catalyzed a paradigm shift from host-centric models to information-centric networking. A critical challenge in this transition is the scalability of routing and the efficiency of content distribution within massive, dynamically changing network topologies. This paper investigates the application of hyperbolic graph embedding techniques to address these multidimensional challenges. Unlike traditional Euclidean spaces, hyperbolic space intrinsically models the hierarchical and scale-free properties ubiquitous in complex network graphs, mirroring the exponential expansion of network trees within a bounded geometry. By mapping network nodes and content objects into a continuous hyperbolic space, we facilitate highly efficient, greedy geographic routing mechanisms that significantly reduce routing table sizes, mitigate forwarding state explosion, and minimize communication overhead. We propose a comprehensive framework that integrates hyperbolic embedding with information-centric routing protocols, optimizing both path discovery and distributed caching strategies. Through extensive theoretical analysis and architectural design, we demonstrate how this continuous non-Euclidean geometry enables high-fidelity representations of complex topologies with minimal dimensional requirements. The embedding preserves latent network hierarchies and optimizes content retrieval latencies by aligning the physical network topology with logical content namespaces. Experimental evaluations validate that the hyperbolic routing scheme consistently outperforms state-of-the-art Euclidean and purely topological methods in terms of routing stretch, path success rate, and memory consumption. This research establishes a robust mathematical and practical foundation for next-generation scalable content distribution networks, offering a viable trajectory for the deployment of globally distributed information-centric architectures.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in the Journal of the Artificial Intelligence Engineers Consortium (JAIEC) are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license permits unrestricted use, distribution, reproduction, and adaptation in any medium, provided the original author(s) and source are properly credited.
Authors retain copyright of their work while granting the journal the right of first publication.
References
1. Xiao, W., Tan, Q., Chen, M., Yao, Y., & Shu, F. (2026). Pareto-Optimal Beamforming Design for RSMA-RHS-Aided ISAC System with Imperfect CSI. IEEE Transactions on Vehicular Technology.
2. Hu, Y., Yang, Z., Zhao, C., & Ji, W. (2026, May). Adaptive guidance semantically enhanced via multimodal llm for edge-cloud object detection. In ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 12962-12966). IEEE.
3. Yang, Y., Ma, X., Li, C., Zheng, Z., Zhang, Q., Huang, G., ... & Zhao, Q. (2021). Believe what you see: Implicit constraint approach for offline multi-agent reinforcement learning. Advances in Neural Information Processing Systems, 34, 10299-10312.
4. Ou, J., Guo, J., Jiang, S., Li, X., Xue, R., Tian, W., & Buyya, R. (2025). Accelerating long-context inference of large language models via dynamic attention load balancing. Knowledge-Based Systems, 115018.
5. Xia, L., Yang, M., Wang, J., Yan, Z., Ren, Y., Yu, G., & Lei, K. (2025, September). Mamba4Net: Distilled Hybrid Mamba Large Language Models For Networking. In2025 IEEE 33rd International Conference on Network Protocols (ICNP)(pp. 1-10). IEEE.
6. Yao, Y., Zhu, Z., Miao, P., Cheng, X., Shu, F., & Wang, J. (2025). Optimizing hybrid RIS-aided ISAC systems in V2X networks: A deep reinforcement learning method for anti-eavesdropping techniques. IEEE Transactions on Vehicular Technology, 74 (6), 9224-9239.
7. Xu, C., Wang, C., & Li, Z. (2026). H2-LBM: A Hierarchical Hybrid Deep Reinforcement Learning Framework for L7 Load Balancing and Global Traffic Scheduling in Multi-Cloud LLM Serving. Fundamental Scientific Reports in Multidisciplinary Areas, 2 (02), 109-120.
8. Wan, H., Zhang, Y., Wang, J., Wu, D., Li, M., Chen, X., ... & Ji, X. (2025). Toward universal embodied planning in scalable heterogeneous field robots collaboration and control. Journal of Field Robotics, 42(5), 2318-2336.
9. Tian, Y., & Yang, Z. (2026, May). SAEC: scene-aware enhanced edge-cloud collaborative industrial vision inspection with multimodal LLM. In ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 19747-19751). IEEE.
10. Li, X., Wang, P., Li, G., Ni, L., & Zhang, Y. (2023). Design of interface circuits and lightweight PUF for TMR sensors. IEEE Sensors Journal, 23(11), 11754-11761.
11. Yang, W., Qin, Y., & Yang, Z. (2022). A reinforcement learning based data storage and traffic management in information-centric data center networks. Mobile Networks and Applications, 27(1), 266-275.
12. Yao, Y., Sun, J., Miao, P., Chen, G., & Tafazolli, R. (2026). Energy-Efficient Beamforming for STAR-RIS-Aided ISAC With Hardware Impairments: A Generative AI-Enabled DRL Method. IEEE Transactions on Wireless Communications.
13. Yang, W., Qin, Y., Jiang, Z., & Chu, X. (2021, June). Traffic Management for Distributed Machine Learning in RDMA-enabled Data Center Networks. In ICC 2021-IEEE International Conference on Communications (pp. 1-6). IEEE.
14. Tao, J., Lyu, R., & Cao, X. (2026). A Scalable Data Governance Architecture for Privacy-Aware Intelligent Learning Systems in Lifelong. Future-Adaptive Intelligence and Lifelong Systems, 1 (1).
15. Yang, Z., Ji, W., & Wang, Z. (2024). Adaptive joint configuration optimization for collaborative inference in edge-cloud systems. Science China Information Sciences, 67(4), 149103.
16. Sigelman, B. H., Barroso, L. A., Burrows, M., Stephenson, P., Plakal, M., Beaver, D., et al. (2010). Dapper, a large-scale distributed systems tracing infrastructure. Technical Report, Google.
17. Yang, Z., Ji, W., Guo, Q., & Wang, Z. (2023, October). Javp: Joint-aware video processing with edge-cloud collaboration for dnn inference. In Proceedings of the 31st ACM International Conference on Multimedia (pp. 9152-9160).
18. Li, Y., Zhou, Y., Zhang, Y., Wang, P., Zhang, Y., Li, X., & Zhang, X. (2025). Nonvolatile Optical Switch With a Programmable pn Heterojunction.IEEE Transactions on Electron Devices,72(8), 4030-4035.
19. Li, X., Yin, L., Chen, W., Liu, X., & Fu, Q. (2016). Study of a three-axis digital tunneling resistance-type magnetic sensor.IEICE Electronics Express,13(13), 20160319-20160319.
20. Yang, Z., Liang, B., & Ji, W. (2021). An intelligent end–edge–cloud architecture for visual IoT-assisted healthcare systems. IEEE internet of things journal, 8(23), 16779-16786.
21. Wuyang Zhang and Shichao Pei. 2026. Your LLM Agent Can Leak Your Data: Data Exfiltration via Backdoored Tool Use. arXiv:2604.05432 [cs.CR] doi:10.48550/arXiv.2604.05432
22. Wang, S., Zhang, X., Wang, P., & Li, X. (2026). Design of Magnetic Sensor Array-Based PUFs for IoT Security. IEEE Transactions on Instrumentation and Measurement, 75, 1-9.
23. Zhang, W., Li, M., & Chen, X. (2026). Distributed Edge-Fog-Cloud Architecture for Intelligent Fault Diagnosis of Permanent Magnet Synchronous Motors Using Temporal Fusion Transformers and Federated Learning. Fundamental Scientific Reports in Multidisciplinary Areas, 2 (02), 121-131.
24. Li, X., Yin, L., Chen, W., Gao, Z., & Liu, X. (2017). A high-resolution tunneling magneto-resistance sensor interface circuit.Modern Physics Letters B,31(04), 1750030.