network centrality analysis

Existing graph centrality measures lack a unified, quantifiable framework for systematic comparison, hindering the formalization and validation of related conjectures. This work proposes a mathematical approach based on vertex rankings to construct the first computable approximation framework capable of systematically comparing any two centrality measures. By integrating graph theory, formal modeling, and approximation algorithms, the method not only verifies several classical conjectures but also generates novel hypotheses of independent research interest. The framework thus establishes a theoretical foundation for network science and opens new avenues for future investigation.

Quantifying social mobility in temporal networks remains challenging due to the dynamic interplay between node-level and neighborhood-level positional evolution. Method: We propose “hierarchical mobility,” defined as the cross-temporal evolution and coupling of node and neighborhood centrality (proxied by degree), and develop a novel triadic statistical framework—comprising mobility, altruism, and community metrics—grounded in temporal modeling, dynamic correlation analysis, and synthetic network generation incorporating preferential attachment and reset mechanisms. We validate the framework across 26 real-world temporal networks. Contribution/Results: Our approach enables conditional identification of the reverse “rich-get-richer” effect—requiring degree inequality as a prerequisite—and establishes an interpretable, discriminative metric system for network structural dynamics. Empirical results reveal strong temporal stability in hierarchical positions, low correlation between individual and neighborhood mobility, and robust cross-domain discriminability of network architectures.

Network science faces systemic challenges—including definitional redundancy, inconsistent nomenclature, poor discoverability, and difficulty in empirical validation—due to the proliferation of over 400 centrality measures. To address this, we introduce the first comprehensive centrality knowledge base, grounded in a unified classification framework that integrates mathematical formulation and semantic interpretation. Our methodology combines bibliometric analysis, taxonomic modeling, and knowledge graph construction. We further develop Centrality Zoo, an open-source, interactive platform enabling multidimensional search, visual comparative analysis, and algorithmic reproducibility. This work establishes standardized representations, transparent evaluation protocols, and community-driven curation for centrality measures. As a result, it significantly enhances reproducibility and interpretability in metric selection for complex network analysis, providing foundational infrastructure for quantifying node and edge importance in networks.

To address the challenges of modeling time-varying user influence and identifying cross-community key propagators in polarized social networks, this paper proposes a community-aware temporal centrality framework. Methodologically, it introduces an enhanced temporal degree centrality metric and develops a temporal independent cascade propagation model that explicitly incorporates opinion drift and participation decay. Crucially, it innovates an “influence hierarchy band” mechanism to enable hierarchical tracking and aggregation of node-level influence across communities. Experimental evaluations on multiple real-world polarized networks demonstrate that the framework consistently discriminates influence hierarchies and significantly improves the accuracy of identifying cross-community influential spreaders. The approach offers an interpretable and scalable computational paradigm for analyzing information diffusion dynamics and guiding influence-aware governance in polarized environments.

This paper addresses the limitations of text-based approaches for interdisciplinary literature identification—namely, high computational cost and poor interpretability—by proposing a purely network-structural method. It models citation networks as directed acyclic graphs (DAGs) and introduces “diversity centrality,” a novel metric that integrates transitive reduction with degree centrality to identify pivotal papers bridging densely connected, multi-disciplinary subgroups. By applying topological reduction to eliminate redundant transitive paths, the method accentuates cross-domain hub papers. Experiments across multiple real-world citation networks demonstrate that the approach achieves interdisciplinary impact detection performance comparable to state-of-the-art text-analytic methods, while being computationally efficient, parameter-free, and fully interpretable. It thus establishes a scalable, transparent, and structurally grounded paradigm for assessing interdisciplinary research.

This study quantifies the impact of non-code contributions—such as network position, temporal activity patterns, and code review behavior—on contributor influence within open-source ecosystems. Leveraging 25 years of project data from the Cloud Native Computing Foundation, the work presents the first systematic integration of graph neural networks, temporal network analysis, and multidimensional contribution metrics. The authors develop GPU-accelerated implementations of PageRank and betweenness centrality, alongside a custom LSTM model, to identify five distinct contributor roles. Findings reveal that the top 1% of contributors disproportionately shape structural influence, with “Bridge”-type roles proving critical to network connectivity. The research further demonstrates that network metrics evolve significantly around project milestones and proposes a role-based framework for assessing community health.

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.

Citation disparities in AI top-tier conferences persist despite controlling for paper quality, suggesting unobserved structural drivers. Method: Leveraging 17,942 papers from NeurIPS, ICML, and ICLR (2005–2024), we propose Harmonic Closeness Temporal Centrality with Decay (HCTCD)—a temporal, collaboration-strength-weighted centrality measure—and introduce Beta regression to model citation percentile ranks. Contribution/Results: We demonstrate that team-level exponentially weighted centrality aggregation substantially outperforms individual- or rank-based aggregation; long-term centrality exerts significantly greater influence than short-term metrics. Integrating HCTCD reduces mean squared error in citation prediction by 2.4%–4.8%. This work provides the first systematic empirical evidence that network structural bias—rather than content quality alone—dominates citation distribution in AI research, offering both novel interpretability and a quantifiable, fairness-aware tool for scholarly evaluation.

This paper addresses the challenge of modeling degree sequences in multidisciplinary citation networks, where community heterogeneity—such as disparities in growth rates, reference list lengths, and preferential citation tendencies—complicates traditional modeling. We propose an extended 3DSI model that integrates heterogeneous growth, preferential attachment, and continuous-time Markov processes, augmented with extreme-value statistics theory. For the first time, we analytically prove that within-community citation distributions converge to the Pareto Type II distribution. This derivation yields interpretable, closed-form metrics for inequality (Gini coefficient) and preference, along with a principled parameter estimation framework. Empirical validation on real-world multidisciplinary citation data demonstrates that our model significantly outperforms the classical Price model, achieving high-fidelity cross-disciplinary degree distribution fitting, quantitative comparison of citation inequality across fields, and mechanistic attribution of underlying drivers.

Traditional citation networks treat all references uniformly, making it difficult to identify the core sources that genuinely inspire a study and thereby compromising the accuracy of impact assessment. This work proposes a novel approach that systematically leverages large language models (LLMs) with two prompting strategies to automatically detect seminal citations from full-text articles, constructing a backbone citation network that captures the essential structure of scientific knowledge. Analyses reveal that, although smaller in scale, this backbone network exhibits non-random topology with higher heterogeneity in in-degree distribution. Its topological properties—such as modularity, transitivity, and degree assortativity—systematically differ from those of the full citation network. Nevertheless, rankings of highly cited papers and authors show strong consistency between the two networks, suggesting that despite containing redundancy, the full network remains effective in reflecting relative scholarly influence.