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[2507.11732] Graph Neural Networks Powered by Encoder Embedding for Improved Node Learning
Summary
This paper introduces a novel framework for Graph Neural Networks (GNNs) that utilizes a one-hot graph encoder embedding (GEE) to enhance node feature initialization, leading to improved performance in various graph learning tasks.
Why It Matters
The study addresses a critical limitation in GNNs related to feature initialization, which can significantly affect model performance. By proposing a structure-aware initialization method, the research contributes to more efficient and stable GNN architectures, which is essential for applications across machine learning and data science.
Key Takeaways
- GNN performance is highly dependent on the quality of initial feature representations.
- The proposed GEE provides a statistically grounded initialization that enhances model stability and convergence.
- GG framework shows substantial performance improvements in node classification tasks, achieving 10-50% accuracy gains.
- Integrating GEE into GNNs allows for better exploitation of graph topology from the outset.
- The research emphasizes the importance of principled initialization in machine learning models.
Computer Science > Machine Learning arXiv:2507.11732 (cs) [Submitted on 15 Jul 2025 (v1), last revised 21 Feb 2026 (this version, v2)] Title:Graph Neural Networks Powered by Encoder Embedding for Improved Node Learning Authors:Shiyu Chen, Cencheng Shen, Youngser Park, Carey E. Priebe View a PDF of the paper titled Graph Neural Networks Powered by Encoder Embedding for Improved Node Learning, by Shiyu Chen and 3 other authors View PDF HTML (experimental) Abstract:Graph neural networks (GNNs) have emerged as a powerful framework for a wide range of node-level graph learning tasks. However, their performance typically depends on random or minimally informed initial feature representations, where poor initialization can lead to slower convergence and increased training instability. In this paper, we address this limitation by leveraging a statistically grounded one-hot graph encoder embedding (GEE) as a high-quality, structure-aware initialization for node features. Integrating GEE into standard GNNs yields the GEE-powered GNN (GG) framework. Across extensive simulations and real-world benchmarks, GG provides consistent and substantial performance gains in both unsupervised and supervised settings. For node classification, we further introduce GG-C, which concatenates the outputs of GG and GEE and outperforms competing methods, achieving roughly 10-50% accuracy improvements across most datasets. These results demonstrate the importance of principled, structure-aware initializa...
