co-authorship network analysis

This paper addresses the link prediction task of future collaborator recommendation in academic social networks. To overcome the limitations of existing approaches—which heavily rely on topological structures while neglecting semantic and performance-related author attributes—we propose a supervised method integrating multidimensional node features. Specifically, we systematically introduce four complementary node-level features: (1) research interest similarity, derived from topic embeddings; (2) institutional affiliation similarity, computed via hierarchical institutional encoding; and (3–4) two aggregated research performance metrics, including h-index and publication count. We evaluate our approach using XGBoost and Random Forest classifiers on the ArnetMiner and DBLP datasets. Experimental results demonstrate statistically significant improvements in accuracy and AUC over baseline methods, confirming the effectiveness and generalizability of incorporating semantic and performance features for large-scale academic link prediction.

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.

This study investigates the knowledge structure and interdisciplinary evolution of social network research. Methodologically, it constructs co-authorship and citation dual networks by systematically comparing three authoritative journals—*Social Networks*, *Network Science*, and *Journal of Complex Networks*—and applies multidimensional centrality measures (degree, betweenness, and eigenvector centrality) to identify pivotal scholars, foundational publications, and bridging journals. It proposes, for the first time, a multi-centrality-based approach to mapping cross-domain knowledge structures, enabling precise characterization of disciplinary boundaries and integration nodes. Results reveal a three-dimensional evolutionary trajectory in network science: from social empiricism → dynamical modeling → mathematical foundations. This provides empirical evidence and methodological support for understanding knowledge convergence mechanisms and the dynamic reconfiguration of disciplinary boundaries.

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.

In citation networks, temporal constraints induce asymmetry in the adjacency matrix (with missing lower-triangular entries), leading to unidentifiable common factors in standard factorization models. Method: We propose a dual-space common-factor model that separately encodes citing and cited behaviors of papers, establishing the first identifiable common-factor decomposition framework for asymmetric, time-constrained adjacency matrices, and designing a memory-efficient, time-aware matrix completion algorithm. Contribution/Results: Evaluated on the largest statistical literature dataset to date—256,000 papers spanning 1898–2024—we achieve joint embedding and topic modeling for high-dimensional sparse citation networks. Our approach uncovers 11 semantically coherent and interpretable subfield common-factor structures, advancing temporal network representation learning and scientometric analysis with a novel, theoretically grounded paradigm.

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.

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.

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.

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.