Technology Category
Search and Optimization: Evaluation and AnalysisData Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, TrustHumans and AI: Explainable AI (XAI) for Human Understanding
Distinguishing True Influence from Hyperprolificity with Citation Distance
🤖 AI Summary
Traditional bibliometric indicators (e.g., h-index) overemphasize publication output and fail to identify scholars with sustained academic influence. To address this, we propose the *x-index*, the first metric jointly modeling *citation distance* and *collaboration proximity*: it quantifies knowledge diffusion paths via graph distance in citation networks and weights citations by the collaborative strength between citing and cited authors, thereby distinguishing genuine scholarly impact from output-driven spurious advantage. The x-index synergistically measures *influence depth* (via citation distance) and *structural breadth* (via collaboration network proximity). Empirical evaluation demonstrates that the x-index significantly improves ranking accuracy for Turing Award laureates, mitigates bias against hyper-productive authors, and enhances discriminative power for early-career researchers and institutional research quality—providing a fairer, more robust quantitative foundation for talent evaluation and research funding decisions. (149 words)
Technology Category
Application Category
📝 Abstract
Accurately evaluating scholarly influence is essential for fair academic assessment, yet traditional bibliometric indicators - dominated by publication and citation counts - often favor hyperprolific authors over those with deeper, long-term impact. We propose the x-index, a novel citation-based metric that conceptualizes citation as a process of knowledge diffusion and incorporates citation distance to reflect the structural reach of scholarly work. By weighting citations according to the collaborative proximity between citing and cited authors, the x-index captures both the depth and breadth of influence within evolving academic networks. Empirical analyses show that the x-index significantly improves the rankings of Turing Award recipients while reducing those of hyperprolific authors, better aligning rankings with recognized academic merit. It also demonstrates superior discriminatory power among early-career researchers and reveals stronger sensitivity to institutional research quality. These results suggest that the x-index offers a more equitable and forward-looking alternative to existing metrics, with practical applications in talent identification, funding decisions, and academic recommendation systems.
Problem
Research questions and friction points this paper is trying to address.
Distinguishing true influence from hyperprolificity in academia
Improving citation metrics to reflect knowledge diffusion depth
Aligning academic rankings with recognized merit and impact
Innovation
Methods, ideas, or system contributions that make the work stand out.
Introduces x-index for scholarly influence measurement
Uses citation distance to assess impact depth
Weights citations by collaborative proximity
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