collaboration network analysis

为了解决动态属性图中链接预测和社区发现的问题，提出了一种新的信息工作流程inc-LPCDAG，结合结构和属性信息分析社交系统。

In practical applications, community detection methods lack standardized evaluation protocols, and their impact on downstream graph mining tasks is often overlooked. This paper systematically investigates how diverse community detection algorithms affect the performance of link prediction and node classification. We propose a unified, extensible evaluation framework that integrates structured community feature extraction, statistical analysis, and machine learning modeling to enable cross-algorithm performance comparison. Experimental results across multiple benchmark datasets demonstrate that algorithm selection significantly influences downstream task accuracy, with distinct methods exhibiting pronounced strengths and weaknesses depending on the specific task. Our framework provides reproducible, empirically grounded guidance for selecting appropriate community detection methods tailored to concrete application scenarios, thereby bridging the gap between community detection research and real-world graph analytics. (149 words)

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 investigates the global research collaboration landscape and thematic evolution in AI-empowered human resource management (AI-HRM). Addressing the lack of dynamic, network-informed syntheses in the field, we construct a co-authorship network from 102,000 authors and 288,000 collaborations indexed in Web of Science. Integrating social network analysis (SNA), centrality measures, community detection, and the TOPSIS multi-criteria decision method, we introduce a methodological innovation: simultaneous identification of thematic clusters and geographically anchored collaboration communities. Results reveal four core research themes—AI system identification and control, HR data analytics and performance optimization, machine learning–driven classification and prediction, and AI-augmented strategic decision-making—as well as three national clusters (U.S., China, EU) and five high-impact institutional communities. The study systematically characterizes the structural features of knowledge production and collaborative governance in AI-HRM, advancing beyond static literature review paradigms.

This work systematically investigates recurrent higher-order structural patterns across domains in hypergraphs and establishes an analytical framework capable of generating realistic synthetic hypergraphs. Method: We propose the first unified tripartite taxonomy for hypergraph mining—comprising pattern discovery, analytical tools, and generative models—integrating graph theory, random hypergraph models, statistical significance testing, and higher-order metrics (e.g., hypergraph transitivity). Our toolkit includes null models, substructure identification algorithms, and structural measures; we further design a feature-driven synthetic generator grounded in empirical hypergraph characteristics. Contribution/Results: We introduce the first multidimensional, fine-grained classification scheme and comprehensive research survey of hypergraph mining, explicitly identifying open challenges and interdisciplinary application pathways. This work lays a theoretical foundation and provides practical guidelines for higher-order network analysis, advancing both methodological rigor and real-world applicability in hypergraph science.

This study addresses the challenge of applying conventional statistical methods to collections of heterogeneous networks that vary in size and type and lack node correspondence. To overcome this, the authors propose a functional Topological Data Analysis (funTDA) framework that uniquely integrates functional data analysis with persistent homology to extract topological features from networks. This approach enables standard statistical operations—including mean and variance estimation, principal component analysis, and hypothesis testing—despite the non-Euclidean nature of network structures, thereby establishing a unified inferential framework. Empirical evaluations demonstrate that funTDA effectively discriminates networks with distinct connectivity patterns and successfully uncovers significant topological differences in real-world applications, such as literary co-occurrence networks and influenza gene regulatory networks.

This study presents the first systematic investigation into the network topology of the Egyptian Reddit community, examining user interaction patterns and information diffusion mechanisms. Leveraging a dataset comprising 23,185 users and 105 Egypt-related subreddits, the research employs complex network analysis—including degree distribution and clustering coefficient—to identify influential core users, tightly knit local communities, and dominant connectivity patterns. The findings not only delineate the structural characteristics and information flow pathways of a region-specific online community but also offer empirical insights into the organizational logic of social media communities in non-Western contexts.

This work proposes a formal generalization of role and positional analysis in social systems from traditional graph-based models—limited to pairwise interactions—to hypergraph-based models that accommodate higher-order interactions. Leveraging category theory and a universal coalgebraic framework, the authors rigorously formalize the core notions of roles and positions and systematically extend them to a unified structure encompassing both graphs and hypergraphs. The resulting framework not only subsumes classical graph models as special cases but also establishes theoretical coherence and validity through functoriality theorems. This advancement provides a mathematically rigorous foundation for analyzing higher-order structures in social networks, thereby enabling more nuanced and expressive modeling of complex relational data beyond dyadic ties.

This study addresses the limitation of traditional network analysis—which operates primarily at the node level and fails to capture coexisting community-level structural patterns—by proposing the first community-level core-periphery detection framework tailored to collaborative networks. Methodologically, it jointly optimizes community partitioning and role assignment through an objective function that models both inter-community connection density and strength, enabling attribute-driven interpretation (e.g., disciplinary or geographical) of collective roles. Empirical evaluation on an Italian co-authorship network demonstrates that the framework effectively uncovers hierarchical core-periphery structures tightly linked to institutional status, regional development, and research themes, while quantifying structural inequality in scientific collaboration. By transcending the node-centrality paradigm, this work provides a novel, structurally grounded perspective for analyzing organizational mechanisms underlying knowledge diffusion and innovation emergence.

This study explores the effective integration of large language models (LLMs) into qualitative and mixed-methods social network analysis to augment—rather than replace—the deep analytical capacities of human researchers. Focusing on core issues such as relational meaning, narrative interpretation, and identity construction, the work proposes an LLM application paradigm oriented toward enhancing methodological rigor. This paradigm emphasizes human–AI collaboration, reflexive practice, and ethical accountability. By incorporating LLM-assisted data coding, theory generation, and abductive reasoning, the project develops a methodologically innovative yet practically feasible framework for qualitative social network analysis. The approach significantly improves analytical efficiency while upholding scholarly standards and ethical compliance.