bibliometric analysis

To address citation metric distortion—particularly in the h-index—caused by deliberate manipulation in academic evaluation, this paper proposes a novel, quantifiable method for detecting both small-scale (e.g., self-citation and reciprocal citation within tight-knit groups) and large-scale (e.g., hyper-collaborative network–driven) citation manipulation. It formally defines and distinguishes these two manipulation patterns for the first time. The method introduces three unsupervised detection indicators: h-index anomaly, citation source concentration, and coauthor network scale—overcoming the limitations of single-metric approaches. Leveraging full-disciplinary Scopus data, the study integrates statistical distribution analysis, percentile-based thresholds (1% and 5%), and citation network structural analysis to characterize cross-disciplinary manipulation prevalence. Results yield actionable, empirically grounded detection thresholds that effectively identify anomalously high h-indices inconsistent with scholarly impact, thereby offering a robust new tool for academic integrity assessment.

This study investigates whether low-impact journals exhibit denser and more reciprocal author citation patterns and the resulting distortions in bibliometric indicators. Leveraging Crossref data, journals are stratified by discipline-normalized Eigenfactor percentiles to distinguish low-impact (Case) from high-impact (Control) groups, with author-matching controls employed to compare citation network structures. The analysis reveals that low-impact journals form insular “citation economies,” manifesting a pronounced bifurcation into “two worlds.” It further identifies 277 high-purity anomalous citation clusters characterized by core–periphery topologies. Findings indicate that authors in low-impact journals display 6.7 times higher mutual citation rates, 4.7 times greater reciprocity, and an 11-fold increase in citation clique strength, confirming the presence of directed citation flows rather than equitable mutual referencing.

Current approaches to research impact assessment predominantly rely on single metrics such as citation counts, which fail to comprehensively capture multidimensional aspects of scholarly influence—including overall impact, temporal dynamics, early momentum, and field-normalized performance. Moreover, there is a notable absence of open-source tools capable of supporting large-scale citation graph computations. To address these limitations, this work proposes an open-source software library built on Apache Spark that leverages optimized graph algorithms and distributed computing to enable efficient, scalable parallel computation of multidimensional influence metrics across academic graphs comprising tens of billions of publications and hundreds of billions of citations. This framework overcomes the scalability and dimensionality constraints of existing methods, substantially enhancing the feasibility, flexibility, and practicality of large-scale research evaluation.

This work proposes BIP! Scholar, a novel researcher profiling platform that addresses the limitations of existing systems, which predominantly focus on publications and bibliometric indicators and thus fail to capture the full spectrum of scholarly contributions in context. BIP! Scholar introduces a template-driven architecture that enables researchers to flexibly construct structured profiles based on trajectories, narratives, or hybrid models, tailored to specific evaluation or presentation needs. By integrating customizable templates, multi-faceted research activity modeling, and a user-configurable interface, the platform dynamically represents non-traditional outputs, roles, and scholarly activities. This approach significantly enhances the contextual adaptability and evaluative utility of researcher profiles and empowers assessment experts to design and test new profiling templates.

Conventional citation-based metrics fail in large-language-model (LLM) papers with thousands of co-authors, undermining fair assessment of individual scholarly contribution. Method: This paper introduces the Scaled Balanced Contribution Index (SBCI), a novel author-level bibliometric metric that uniformly models differential contributions across both large-scale and small-scale publications. SBCI integrates citation network analysis, synthetic data modeling, and theoretical derivation to ensure interpretability and robustness. Contribution/Results: Empirical evaluation on synthetic datasets demonstrates that SBCI significantly outperforms established metrics—including the h-index and CNC—in identifying genuine academic impact. It is particularly effective for evaluating scholars in highly collaborative research settings characteristic of the LLM era, thereby supporting more equitable decisions in academic evaluation, faculty recruitment, and research funding allocation.

This study addresses the limitations of existing academic platforms in providing fine-grained author metadata and geographic visualization, which are either unavailable or prohibitively costly. The authors propose an automated analytical framework that leverages a single Google Scholar user ID and integrates data from five sources—Google Scholar, OpenAlex, CrossRef, Semantic Scholar, and OpenStreetMap—through a five-stage pipeline for paper parsing, author disambiguation, and geocoding. Key innovations include a Unicode-resilient metadata parser, a two-stage institutional similarity–based disambiguation mechanism, and a city-level location repair method using OpenAlex. The system increases city-level geographic coverage for authors from 0% to approximately 60%, reduces h-index attribution errors by up to ninefold, and produces interactive HTML maps alongside structured analytical reports.

This study addresses the overreliance on citation counts in traditional research evaluation, which overlooks the intermediate pathways of knowledge dissemination. It introduces “citation pathways” as a novel dimension in scientometrics and formally defines two key intermediary structures within them: Interpretive Knowledge Nodes (IKNs) and Citation Compression Layers (CCLs). By integrating normative citation structure analysis, thought experiments, and a simplified “citation gravity” model, the work reveals how artificial intelligence reshapes the production costs of citable knowledge intermediaries and alters the evolutionary dynamics of citation networks. The findings demonstrate that, under compliant citation practices, the positional effects of entities within these pathways significantly influence the validity of impact assessments, highlighting potential misalignments in institutional incentives under extreme conditions and thereby redefining the boundaries of academic impact measurement.

This study addresses the limitations of traditional bibliometric systems, which rely on outdated assumptions of small-scale scholarly communities and struggle to fairly assess contributions or ensure quality control in high-output, large-scale research environments, while remaining vulnerable to strategic manipulation. To overcome these challenges, the authors propose a novel academic evaluation framework based on continuous contribution shares. This approach replaces discrete author ordering with tradable contribution shares, integrating weighted citations and an academic market mechanism to foster a dynamic, long-term-oriented assessment system. Innovatively, it employs a dual-graph structure—comprising a share graph and a citation graph—to model scholarly contribution as a quantifiable and tradable continuous variable, augmented by modular correction factors and an academic capital market model. The resulting academic capital metrics naturally extend across institutional, geographic, disciplinary, and temporal dimensions, effectively curbing credit inflation and incentivizing high-quality research with enduring impact.

This work addresses critical limitations of traditional science, technology, and innovation (STI) analysis—namely its reliance on static indicators that suffer from time lags, shallow semantic representation, and an inability to capture the nonlinear dynamics of knowledge ecosystems. To overcome these challenges, the study proposes a validation-centric, five-layer hybrid framework that constructs a dynamic, versioned knowledge graph from open scholarly data and integrates constrained large language models (LLMs) for structured semantic enrichment. Reliability is ensured through a multi-tiered validation pipeline combining structural, evidential, comparative, and expert-based verification, augmented by a traceability mechanism. This approach significantly enhances the semantic depth, timeliness, and credibility of STI analysis while upholding scientific evidentiary standards, thereby enabling robust detection of emerging trends, mapping of technology transfer pathways, and policy-relevant gap analysis.

This study addresses the limitations of traditional bibliometric indicators, which overlook academic network topology and struggle to detect collusive research misconduct. The authors propose a novel anomaly detection framework that constructs a heterogeneous, multivariate graph from OpenAlex data, incorporating seven node and edge types. By integrating network projection, interpretable structural metrics, community detection, and three specialized anomaly pattern filters, the method generates a ranked list of suspicious entities accompanied by explicit structural evidence—without relying on binary classification. The approach uncovers metric confounding issues and demonstrates that graph-based prestige measures exhibit strong robustness against citation manipulation, outperforming conventional metrics by an order of magnitude in resilience. Experiments successfully reconstruct research teams and identify interdisciplinary bridges in VSB University data; journal-level analysis confirms disciplinary breadth as a reliable signal (AUC=0.70). An open-source tool, apnet, enables minute-scale analyses.