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- W4384575265 abstract "The accuracy of graph representation learning is highly dependent on the precise characterization of node relationships. However, representing the complex and diverse networks in the real world using a single type of node or link is challenging, often resulting in incomplete information. Moreover, different types of nodes and links convey rich information, which makes it difficult to design a graph network that can integrate diverse links. This paper introduces a novel multi-view and multi-layer attention model designed to optimize node embeddings for semi-supervised node classification. The proposed model exploits various types of inter-node links and employs the Hilbert–Schmidt independence criterion to maximize the dissimilarity between distinct node relationships. Furthermore, the multi-layer attention mechanism is used to discern the impact of different neighboring nodes and relationships between various node relationships. The performance of the proposed model, MVMA-GCN, was assessed on numerous real-world multi-view datasets. It was observed that MVMA-GCN consistently outperformed existing models, demonstrating superior accuracy in semi-supervised classification tasks. We have made our code publicly available at here to ensure the reproducibility of our results." @default.
- W4384575265 created "2023-07-18" @default.
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- W4384575265 date "2023-11-01" @default.
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- W4384575265 title "MVMA-GCN: Multi-view multi-layer attention graph convolutional networks" @default.
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- W4384575265 doi "https://doi.org/10.1016/j.engappai.2023.106717" @default.
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