social graph 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。

Detecting coordinated disinformation campaigns involving both AI and human agents in social networks remains challenging, as conventional diffusion models fail to capture complex coordination structures—such as multi-path propagation, branching, and loops. Method: This work introduces a process-mining–based approach to identify coordinated behavioral patterns. Specifically, it adapts process discovery algorithms (e.g., Inductive Miner) to social network metadata—particularly post timestamps—to automatically uncover latent control-flow structures; these are then integrated with temporal graph modeling to enable interpretable behavioral provenance tracing. Contribution/Results: Evaluated on real-world Twitter/X event data, the method significantly improves discrimination accuracy between AI-driven and human-initiated dissemination patterns. It establishes a novel paradigm for malicious coordination detection that jointly achieves high precision and model interpretability—addressing critical gaps in transparency and accountability within automated disinformation analysis.

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)

Existing node-link diagrams struggle to simultaneously represent the four-dimensional temporal evolution—relationship strength, functional role, structural position, and content—in egocentric networks. To address this, we propose a story-line-based micro-visualization framework that pioneers the integration of a metro-map metaphor with customizable attribute encoding, enabling coupled dynamic analysis across all four dimensions. We establish the first task taxonomy specifically designed for egocentric network exploration from the primary actor’s perspective, and integrate topological layout encoding with interactive temporal navigation mechanisms. The framework is empirically validated across three real-world scenarios: disease surveillance, social media trend analysis, and academic career evolution. A usability study confirms its effectiveness in significantly improving both the efficiency and depth of understanding multidimensional dynamic relationships.

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.

Static snapshot-based approaches fail to capture temporal information flow and the evolution of propagation paths in time-evolving graphs. To address this, we propose the first temporal path tracing system designed for highly dynamic vertices. Our method abandons the conventional static graph snapshot paradigm and instead supports configurable, time-constrained path traversal algorithms, integrated with parallel graph processing and web-based interactive visualization. At the modeling level, we adopt an exact temporal graph representation that preserves fine-grained temporal semantics. Computationally, we enable efficient, low-latency path queries over large-scale evolving graphs. Analytically, our system significantly improves both the accuracy and real-time responsiveness of critical propagation path identification. Empirical evaluation demonstrates that our system outperforms state-of-the-art methods in both query efficiency and interpretability, establishing a new benchmark for temporal path analysis in dynamic networks.

本文探讨了使用列式关系引擎和图查询语言处理大规模图分析问题，证明其性能优于原生图引擎，并指出节点/边模型在关系表中是冗余的。

This work addresses the longstanding tension between usability and expressiveness in graph database analysis tools: conventional business intelligence systems lack native graph reasoning capabilities, while specialized query languages impose steep learning curves and fragment analytical workflows. To bridge this gap, we propose GPQL—a formal, composable, and cross-database-compatible graph query language—and introduce the first no-code visual analytics system that automatically compiles user interactions into valid GPQL queries. By integrating visualization designs centered on graph patterns and relationships, our system substantially lowers the barrier for non-technical users to conduct sophisticated graph analyses. Through a 22-month mixed-methods study in telecommunications and supply chain domains—including MILC-based evaluation—we demonstrate that our approach effectively supports real-world graph exploration workflows employed by professional analysts.

This paper addresses graph representation learning for both static and single-event dynamic networks. Methodologically, it introduces a unified structural-aware embedding framework grounded in latent distance modeling, jointly optimizing homophily, transitivity, and balance within an end-to-end paradigm—thereby eliminating heuristic design and multi-stage pipelines. Notably, it is the first to extend latent distance modeling to single-event dynamic settings, enabling extreme node identification and quantitative assessment of influence dynamics. The key contributions are: (1) a hierarchical, interpretable structural-aware representation; (2) seamless unification of embedding learning across static and dynamic networks; and (3) state-of-the-art performance on community detection, anomaly detection, and temporal influence evaluation—significantly outperforming multi-stage baselines.

This work addresses the high computational cost of community detection in dynamic graphs by proposing an efficient GPU-accelerated method for temporal network analysis. Leveraging the NVIDIA RAPIDS ecosystem, it presents the first GPU-based implementation of modularity optimization and symmetric Bethe–Hessian operator eigendecomposition across multiple graph snapshots. The approach integrates the Leiden algorithm with Dask’s distributed scheduling framework to enable scalable community tracking in large-scale temporal networks. Designed with compatibility for the NetworkX-Temporal interface, the system seamlessly fits into existing graph analytics pipelines. Experimental results demonstrate up to a 1000× speedup over CPU baselines under equivalent workloads, substantially enhancing the efficiency of temporal network analysis in domains such as epidemic spreading, financial systems, and cybersecurity.