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[2602.17104] Simplify to Amplify: Achieving Information-Theoretic Bounds with Fewer Steps in Spectral Community Detection
Summary
This paper presents a streamlined spectral algorithm for community detection in the stochastic block model, achieving improved error bounds by simplifying the process.
Why It Matters
The research addresses the complexity in community detection algorithms, proposing a method that enhances computational efficiency while achieving near-optimal performance. This is significant for fields relying on accurate network analysis, such as social networks and data science.
Key Takeaways
- Introduces a simplified spectral algorithm for community detection.
- Achieves improved error bounds approaching information-theoretic limits.
- Demonstrates that simplification can enhance both efficiency and performance.
- Provides theoretical analysis confirming tighter error rates than existing methods.
- Includes comprehensive experimental validation of the proposed approach.
Computer Science > Social and Information Networks arXiv:2602.17104 (cs) [Submitted on 19 Feb 2026] Title:Simplify to Amplify: Achieving Information-Theoretic Bounds with Fewer Steps in Spectral Community Detection Authors:Sie Hendrata Dharmawan, Peter Chin View a PDF of the paper titled Simplify to Amplify: Achieving Information-Theoretic Bounds with Fewer Steps in Spectral Community Detection, by Sie Hendrata Dharmawan and Peter Chin View PDF HTML (experimental) Abstract:We propose a streamlined spectral algorithm for community detection in the two-community stochastic block model (SBM) under constant edge density assumptions. By reducing algorithmic complexity through the elimination of non-essential preprocessing steps, our method directly leverages the spectral properties of the adjacency matrix. We demonstrate that our algorithm exploits specific characteristics of the second eigenvalue to achieve improved error bounds that approach information-theoretic limits, representing a significant improvement over existing methods. Theoretical analysis establishes that our error rates are tighter than previously reported bounds in the literature. Comprehensive experimental validation confirms our theoretical findings and demonstrates the practical effectiveness of the simplified approach. Our results suggest that algorithmic simplification, rather than increasing complexity, can lead to both computational efficiency and enhanced performance in spectral community detection. Co...
