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  • 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 integrates adaptive embedding mechanisms for… 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, a novel knowledge graph embedding… 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 reason over temporal knowledge graphs… More >

  • Open Access


    Improved Density Peaking Algorithm for Community Detection Based on Graph Representation Learning

    Jiaming Wang2, Xiaolan Xie1,2,*, Xiaochun Cheng3, Yuhan Wang2

    Computer Systems Science and Engineering, Vol.43, No.3, pp. 997-1008, 2022, DOI:10.32604/csse.2022.027005


    There is a large amount of information in the network data that we can exploit. It is difficult for classical community detection algorithms to handle network data with sparse topology. Representation learning of network data is usually paired with clustering algorithms to solve the community detection problem. Meanwhile, there is always an unpredictable distribution of class clusters output by graph representation learning. Therefore, we propose an improved density peak clustering algorithm (ILDPC) for the community detection problem, which improves the local density mechanism in the original algorithm and can better accommodate class clusters of different shapes. And we study the… More >

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