Abstract: Skeleton-based action recognition faces class imbalance and insufficient labeling problems in real-world applications. Existing methods typically address these issues separately, lacking a unified framework that can effectively handle both issues simultaneously while considering their inherent relationships. Our theoretical analysis reveals two fundamental connections between these problems. First, class imbalance systematically shifts the eigenvalue spectrum of normalized affinity matrices, compromising both convergence and accuracy of label propagation. Second, boundary samples are critical for model training under imbalanced conditions but are often mistakenly excluded by conventional reliability metrics, which focus on relative class differences rather than holistic connectivity patterns. Built upon these theoretical findings, we propose SpeLER ($\textbf{Spe}$ctral-balanced $\textbf{L}$abel Propagation with $\textbf{E}$nergy-based Tightened $\textbf{R}$eliability), which introduces a spectral balancing technique that explicitly counteracts spectral shifts by incorporating class distribution information. Meanwhile, a propagation energy-based tightened reliability measure is proposed to better preserve crucial boundary samples by evaluating holistic connectivity patterns. Extensive experiments on six public datasets demonstrate that SpeLER consistently outperforms state-of-the-art methods, validating both our theoretical findings and practical effectiveness.