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  • Open Access

    REVIEW

    GNN: Core Branches, Integration Strategies and Applications

    Wenfeng Zheng1, Guangyu Xu2, Siyu Lu3, Junmin Lyu4, Feng Bao5,*, Lirong Yin6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.075741 - 29 January 2026

    Abstract Graph Neural Networks (GNNs), as a deep learning framework specifically designed for graph-structured data, have achieved deep representation learning of graph data through message passing mechanisms and have become a core technology in the field of graph analysis. However, current reviews on GNN models are mainly focused on smaller domains, and there is a lack of systematic reviews on the classification and applications of GNN models. This review systematically synthesizes the three canonical branches of GNN, Graph Convolutional Network (GCN), Graph Attention Network (GAT), and Graph Sampling Aggregation Network (GraphSAGE), and analyzes their integration pathways More >

  • Open Access

    ARTICLE

    Attack Behavior Extraction Based on Heterogeneous Cyberthreat Intelligence and Graph Convolutional Networks

    Binhui Tang1,3, Junfeng Wang2,*, Huanran Qiu3, Jian Yu2, Zhongkun Yu2, Shijia Liu2,4

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 235-252, 2023, DOI:10.32604/cmc.2023.029135 - 22 September 2022

    Abstract The continuous improvement of the cyber threat intelligence sharing mechanism provides new ideas to deal with Advanced Persistent Threats (APT). Extracting attack behaviors, i.e., Tactics, Techniques, Procedures (TTP) from Cyber Threat Intelligence (CTI) can facilitate APT actors’ profiling for an immediate response. However, it is difficult for traditional manual methods to analyze attack behaviors from cyber threat intelligence due to its heterogeneous nature. Based on the Adversarial Tactics, Techniques and Common Knowledge (ATT&CK) of threat behavior description, this paper proposes a threat behavioral knowledge extraction framework that integrates Heterogeneous Text Network (HTN) and Graph Convolutional… More >

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