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  • Open Access


    HCRVD: A Vulnerability Detection System Based on CST-PDG Hierarchical Code Representation Learning

    Zhihui Song, Jinchen Xu, Kewei Li, Zheng Shan*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4573-4601, 2024, DOI:10.32604/cmc.2024.049310

    Abstract Prior studies have demonstrated that deep learning-based approaches can enhance the performance of source code vulnerability detection by training neural networks to learn vulnerability patterns in code representations. However, due to limitations in code representation and neural network design, the validity and practicality of the model still need to be improved. Additionally, due to differences in programming languages, most methods lack cross-language detection generality. To address these issues, in this paper, we analyze the shortcomings of previous code representations and neural networks. We propose a novel hierarchical code representation that combines Concrete Syntax Trees (CST)… More >

  • Open Access


    GNN Representation Learning and Multi-Objective Variable Neighborhood Search Algorithm for Wind Farm Layout Optimization

    Yingchao Li1,*, Jianbin Wang1, Haibin Wang2

    Energy Engineering, Vol.121, No.4, pp. 1049-1065, 2024, DOI:10.32604/ee.2023.045228

    Abstract With the increasing demand for electrical services, wind farm layout optimization has been one of the biggest challenges that we have to deal with. Despite the promising performance of the heuristic algorithm on the route network design problem, the expressive capability and search performance of the algorithm on multi-objective problems remain unexplored. In this paper, the wind farm layout optimization problem is defined. Then, a multi-objective algorithm based on Graph Neural Network (GNN) and Variable Neighborhood Search (VNS) algorithm is proposed. GNN provides the basis representations for the following search algorithm so that the expressiveness… More >

  • Open Access


    LC-NPLA: Label and Community Information-Based Network Presentation Learning Algorithm

    Shihu Liu, Chunsheng Yang*, Yingjie Liu

    Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 203-223, 2023, DOI:10.32604/iasc.2023.040818

    Abstract Many network presentation learning algorithms (NPLA) have originated from the process of the random walk between nodes in recent years. Despite these algorithms can obtain great embedding results, there may be also some limitations. For instance, only the structural information of nodes is considered when these kinds of algorithms are constructed. Aiming at this issue, a label and community information-based network presentation learning algorithm (LC-NPLA) is proposed in this paper. First of all, by using the community information and the label information of nodes, the first-order neighbors of nodes are reconstructed. In the next, the More >

  • Open Access


    Heterophilic Graph Neural Network Based on Spatial and Frequency Domain Adaptive Embedding Mechanism

    Lanze Zhang, Yijun Gu*, Jingjie Peng

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1701-1731, 2024, DOI:10.32604/cmes.2023.045129

    Abstract Graph Neural Networks (GNNs) play a significant role in tasks related to homophilic graphs. Traditional GNNs, based on the assumption of homophily, employ low-pass filters for neighboring nodes to achieve information aggregation and embedding. However, in heterophilic graphs, nodes from different categories often establish connections, while nodes of the same category are located further apart in the graph topology. This characteristic poses challenges to traditional GNNs, leading to issues of “distant node modeling deficiency” and “failure of the homophily assumption”. In response, this paper introduces the Spatial-Frequency domain Adaptive Heterophilic Graph Neural Networks (SFA-HGNN), which… More >

  • Open Access


    Heterogeneous Network Embedding: A Survey

    Sufen Zhao1,2, Rong Peng1,*, Po Hu2, Liansheng Tan2

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 83-130, 2023, DOI:10.32604/cmes.2023.024781

    Abstract Real-world complex networks are inherently heterogeneous; they have different types of nodes, attributes, and relationships. In recent years, various methods have been proposed to automatically learn how to encode the structural and semantic information contained in heterogeneous information networks (HINs) into low-dimensional embeddings; this task is called heterogeneous network embedding (HNE). Efficient HNE techniques can benefit various HIN-based machine learning tasks such as node classification, recommender systems, and information retrieval. Here, we provide a comprehensive survey of key advancements in the area of HNE. First, we define an encoder-decoder-based HNE model taxonomy. Then, we systematically More > Graphic Abstract

    Heterogeneous Network Embedding: A Survey

  • Open Access


    Community Discovery Algorithm Based on Multi-Relationship Embedding

    Dongming Chen, Mingshuo Nie, Jie Wang, Dongqi Wang*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2809-2820, 2023, DOI:10.32604/csse.2023.035494

    Abstract Complex systems in the real world often can be modeled as network structures, and community discovery algorithms for complex networks enable researchers to understand the internal structure and implicit information of networks. Existing community discovery algorithms are usually designed for single-layer networks or single-interaction relationships and do not consider the attribute information of nodes. However, many real-world networks consist of multiple types of nodes and edges, and there may be rich semantic information on nodes and edges. The methods for single-layer networks cannot effectively tackle multi-layer information, multi-relationship information, and attribute information. This paper proposes… More >

  • Open Access


    Critical Relation Path Aggregation-Based Industrial Control Component Exploitable Vulnerability Reasoning

    Zibo Wang1,3, Chaobin Huo2, Yaofang Zhang1,3, Shengtao Cheng1,3, Yilu Chen1,3, Xiaojie Wei5, Chao Li4, Bailing Wang1,3,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2957-2979, 2023, DOI:10.32604/cmc.2023.035694

    Abstract With the growing discovery of exposed vulnerabilities in the Industrial Control Components (ICCs), identification of the exploitable ones is urgent for Industrial Control System (ICS) administrators to proactively forecast potential threats. However, it is not a trivial task due to the complexity of the multi-source heterogeneous data and the lack of automatic analysis methods. To address these challenges, we propose an exploitability reasoning method based on the ICC-Vulnerability Knowledge Graph (KG) in which relation paths contain abundant potential evidence to support the reasoning. The reasoning task in this work refers to determining whether a specific… More >

  • Open Access


    DCRL-KG: Distributed Multi-Modal Knowledge Graph Retrieval Platform Based on Collaborative Representation Learning

    Leilei Li1, Yansheng Fu2, Dongjie Zhu2,*, Xiaofang Li3, Yundong Sun2, Jianrui Ding2, Mingrui Wu2, Ning Cao4,*, Russell Higgs5

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3295-3307, 2023, DOI:10.32604/iasc.2023.035257

    Abstract The knowledge graph with relational abundant information has been widely used as the basic data support for the retrieval platforms. Image and text descriptions added to the knowledge graph enrich the node information, which accounts for the advantage of the multi-modal knowledge graph. In the field of cross-modal retrieval platforms, multi-modal knowledge graphs can help to improve retrieval accuracy and efficiency because of the abundant relational information provided by knowledge graphs. The representation learning method is significant to the application of multi-modal knowledge graphs. This paper proposes a distributed collaborative vector retrieval platform (DCRL-KG) using… More >

  • Open Access


    Knowledge Graph Representation Learning Based on Automatic Network Search for Link Prediction

    Zefeng Gu, Hua Chen*

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2497-2514, 2023, DOI:10.32604/cmes.2023.024332

    Abstract Link prediction, also known as Knowledge Graph Completion (KGC), is the common task in Knowledge Graphs (KGs) to predict missing connections between entities. Most existing methods focus on designing shallow, scalable models, which have less expressive than deep, multi-layer models. Furthermore, most operations like addition, matrix multiplications or factorization are handcrafted based on a few known relation patterns in several well-known datasets, such as FB15k, WN18, etc. However, due to the diversity and complex nature of real-world data distribution, it is inherently difficult to preset all latent patterns. To address this issue, we propose KGE-ANS, More >

  • Open Access


    Future Event Prediction Based on Temporal Knowledge Graph Embedding

    Zhipeng Li1,2, Shanshan Feng3,*, Jun Shi2, Yang Zhou2, Yong Liao1,2, Yangzhao Yang2, Yangyang Li4, Nenghai Yu1, Xun Shao5

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2411-2423, 2023, DOI:10.32604/csse.2023.026823

    Abstract Accurate prediction of future events brings great benefits and reduces losses for society in many domains, such as civil unrest, pandemics, and crimes. Knowledge graph is a general language for describing and modeling complex systems. Different types of events continually occur, which are often related to historical and concurrent events. In this paper, we formalize the future event prediction as a temporal knowledge graph reasoning problem. Most existing studies either conduct reasoning on static knowledge graphs or assume knowledges graphs of all timestamps are available during the training process. As a result, they cannot effectively… More >

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