We lay down the foundations of the Eigenvalue Method in coding theory. The method uses modern algebraic graph theory to derive upper bounds on the size of error-correcting codes for various metrics, addressing major open questions in the field. We identify the core assumptions that allow applying the Eigenvalue Method, test it for multiple well-known classes of error-correcting codes, and compare the results with the best bounds currently available. By applying the Eigenvalue Method, we obtain new bounds on the size of error-correcting codes that often improve the state of the art. Our results show that spectral graph theory techniques capture structural properties of error-correcting codes that are missed by classical coding theory approaches.

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doi.org/10.4153/S0008414X25101600
Canadian Journal of Mathematics
creativecommons.org/licenses/by/4.0
Intelligent and autonomous systems

Peters, L., Abiad, A., & Ravagnani, A. (2025). The Eigenvalue Method in coding theory. Canadian Journal of Mathematics, 2025, 1–41. doi:10.4153/S0008414X25101600