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EigenScore: A Spectral Centrality Framework

Updated 15 October 2025
  • EigenScore is a spectral centrality metric defined by eigenvalue analysis of matrices, used to assess consistency, diversity, or influence across various domains.
  • Key formulations include covariance log-determinant for LLMs, H-eigenscore in hypergraphs for epidemic control, and coupled eigenvector methods in citation networks.
  • Applications span bibliometrics, epidemic control, data visualization, and generative model safety, providing robust and interpretable insights.

EigenScore is a spectral centrality metric whose specific form and semantics differ by domain but is unified in its use of the eigenvalue spectrum of key matrices—covariance, adjacency, or mixed feature matrices—to quantify consistency, diversity, or influence in systems ranging from citation networks to LLMs, hypergraphs, data visualizations, and generative models. Across its instantiations, EigenScore characterizes the “spread” or information content present in high-dimensional representations, serving roles in ranking (bibliometrics), uncertainty quantification (diffusion and LLMs), detection (OOD and hallucination), and policy design (epidemics).

1. Mathematical Formulations and Spectral Foundations

The essential principle underlying EigenScore is its connection to the eigenvalues of context-specific matrices, with prominent examples including:

EigenScore=1Klogdet(C+αIK)\text{EigenScore} = \frac{1}{K}\log \det(C + \alpha I_K)

or equivalently as:

EigenScore=1Ki=1Klogλi\text{EigenScore} = \frac{1}{K} \sum_{i=1}^K \log \lambda_i

where λi\lambda_i are eigenvalues, KK is the number of outputs, and α\alpha is a regularizer.

  • Adjacency tensor H-eigenscore: In epidemic containment on uniform hypergraphs (Jhun, 2021), a symmetric dd-order adjacency tensor aa yields the H-eigenvector ee, computed via nonlinear equations:

i2,,idai1i2idei2eid=λei1d1\sum_{i_2,\ldots,i_d} a_{i_1 i_2 \ldots i_d} e_{i_2} \cdots e_{i_d} = \lambda e_{i_1}^{d-1}

The H-eigenscore for a hyperedge {i1,,id}\{i_1,\ldots,i_d\} is

H-eigenscore({i1,,id})=ei1ei2eid\text{H-eigenscore}(\{i_1,\ldots,i_d\}) = e_{i_1}e_{i_2}\cdots e_{i_d}

  • Coupled network eigenscore: For citation networks (Ujum et al., 2015), the dual eigenvector formulation operates on the coupled author-paper matrix WW and the citation matrix CC:

x(k)=WCTWTx(k1),y(k)=CTWTx(k)x^{(k)} = W C^T W^T x^{(k-1)}, \qquad y^{(k)} = C^T W^T x^{(k)}

yielding mutually reinforced scores for authors and papers.

  • Spectral consensus for visualization: In data visualization (Ma et al., 2022), for multiple candidate projections, the eigenscore is the leading eigenvector (in modulus) of a similarity matrix capturing agreement of local distance profiles, used as weights to synthesize a meta-visualization.

These formulations link EigenScore to notions of differential entropy, spectral radius, and mutual reinforcement, where higher scores typically correspond to greater uncertainty, diversity, centrality, or influence depending on application.

2. Domain-Specific Algorithms and Workflows

EigenScore is computed via the CAPS algorithm, using normalized author-paper matrices and citation graphs, with iterative execution of matrix product updates that propagate fractional credit and citation impact. The algorithm solves for principal eigenvectors under Perron-Frobenius conditions; convergence indicates stable rankings reflecting both productivity and influence.

EigenScore is extended via H-eigenvector analysis of the hypergraph’s adjacency tensor. The leading H-eigenvector is computed via iterative power methods; hyperedges are scored by the product over their member node eigenvector entries. Immunization is prioritized for hyperedges with highest H-eigenscore, which most strongly support epidemic persistence.

Candidate visualizations produce normalized distance matrices. For each sample, a similarity matrix is constructed over visualizations; its leading eigenvector provides samplewise eigenscores, used as local weights to combine profiles into a meta-distance. Standard embedding algorithms produce the consensus visualization.

Responses are generated for a prompt; internal state activations are extracted (typically sentence-level embeddings from selected layers). The covariance matrix of those embeddings quantifies semantic diversity. EigenScore is computed as the normalized log-determinant. High score signals semantic inconsistency, indicative of hallucination or prompt ambiguity. Efficient EigenScore (EES) (Mohammadzadeh et al., 20 Oct 2024) uses Chebyshev polynomial expansion and stochastic estimate of density of states for rapid approximation. Leave-One-Out EigenScore (LOOE) quantifies individual contribution to diversity.

Posterior covariance is derived via score function and Hessian identity. Leading eigenvalues are estimated via Jacobian-free subspace iteration, leveraging finite-difference directional derivatives. EigenScore is formed by aggregating top eigenvalues across timesteps, Z-score normalized relative to InD statistics.

3. Evaluation Criteria and Comparative Benchmarks

  • Citation metrics: Spearman rank correlation with hh-index ($0.77$ for CAPS vs $0.69$ for CITEX), Gini coefficients (0.99\sim 0.99) measure concentration (Ujum et al., 2015).
  • Epidemic containment: Herd immunity threshold (pcp_c) is minimized when immunizing high H-eigenscore hyperedges. Computational cost and effectiveness are compared to SIP-based (Simultaneous Infection Probability) and EI-based (Edge Epidemic Importance) strategies (Jhun, 2021).
  • Visualization assessment: Concordance is quantified by cosine similarity between consensus and true structure; silhouette index and cluster separation assess empirical quality (Ma et al., 2022).
  • LLM generation space and hallucination: AUROC, Pearson correlation, statistical tests (Welch’s tt-tests), and qualitative alignment to ground-truth relationships (GSSBench) (Chen et al., 6 Feb 2024, Mohammadzadeh et al., 20 Oct 2024, Yu et al., 14 Oct 2025).
  • Diffusion OOD detection: AUROC improvement (up to 5%5\% over baselines) and robustness to near-OOD settings (CIFAR-10 vs CIFAR-100) (Shoushtari et al., 8 Oct 2025).

EigenScore variants consistently outperform standard metrics (perplexity, entropy, token similarity, raw uncertainty) where spectral methods capture richer semantic, structural, or centrality information.

4. Practical Applications and Implications

  • Bibliometric analytics: EigenScore yields balanced author and paper rankings, mitigating bias toward prolific but less cited individuals, and enabling network-aware performance evaluation (Ujum et al., 2015).
  • Epidemic control: H-eigenscore-based immunization guides interventions by targeting hyperedges most essential to contagion persistence, rationalizing public health decisions (Jhun, 2021).
  • Consensus visualization: Sample-specific eigenscore weighting aggregates diverse embeddings, producing modular and robust visualizations agnostic to algorithm and scale (Ma et al., 2022).
  • LLM reliability: EigenScore functions as both a detection and calibration tool for hallucination and ambiguity, interprets model “overthinking,” and steers diversity in generation (Chen et al., 6 Feb 2024, Mohammadzadeh et al., 20 Oct 2024, Yu et al., 14 Oct 2025).
  • Generative model safety: EigenScore-based OOD detection enables robust uncertainty quantification, with efficient implementation in high-dimensional denoising tasks (Shoushtari et al., 8 Oct 2025).

5. Algorithmic Efficiency, Scaling, and Limitations

EigenScore computation is domain-dependent in complexity. CAPS (Ujum et al., 2015) and visualization consensus (Ma et al., 2022) scale with matrix size but are tractable due to normalization and low-rank structure. H-eigenscore (Jhun, 2021) exploits tensor symmetry and can be computed via iterative updates for uniform hypergraphs. Efficient EigenScore (EES) (Mohammadzadeh et al., 20 Oct 2024) leverages Chebyshev expansion and stochastic trace estimation for 2× speedup; Jacobian-free subspace iteration (Shoushtari et al., 8 Oct 2025) circumvents high-dimensional Jacobian formation, allowing leading eigenvalue extraction via forward denoiser evaluations. Limitations include requirement for white-box access (LLMs), focus on dominant eigenvalues (diffusion), and potential sensitivity to layer and sample choice.

6. Extensions, Future Directions, and Interpretability

Extensions proposed include richer exploitation of covariance structure beyond leading eigenvalues (Shoushtari et al., 8 Oct 2025), improved calibration and steering mechanisms for LLMs (Yu et al., 14 Oct 2025), direct integration into regularization or safety objectives, and transfer to multimodal or structured domains. Interpretability arises from differential entropy connection, semantic cluster analysis, and individualized diversity assessment (LOOE). The domain-unifying aspect of EigenScore is its ability to provide interpretable, internally consistent, and empirically validated measures of diversity, uncertainty, or centrality rooted in spectral properties of system-relevant matrices.


In summary, EigenScore is a spectral quantification framework instantiated across bibliometrics (Ujum et al., 2015), epidemic control (Jhun, 2021), visualization assessment (Ma et al., 2022), LLM hallucination detection and calibration (Chen et al., 6 Feb 2024, Mohammadzadeh et al., 20 Oct 2024, Yu et al., 14 Oct 2025), and generative model OOD detection (Shoushtari et al., 8 Oct 2025). Its central mechanisms depend on the eigenvalues of internal or coupling matrices, enabling robust measurement of influence, uncertainty, and diversity with advantages over traditional metrics in both interpretability and empirical performance.

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