Abstract
Graph Neural Networks (GNNs) have attracted extensive interest in the world because of its superior performance in the field of graph representation learning. Most GNNs have a message passing mechanism to update node representations by aggregating and transforming input from node neighbors. The current methods use the same strategy to aggregate information from each feature dimension. However, according to current papers, the model will be more practical if the feature information of each dimension can be treated differently throughout the aggregating process. In this paper, we introduces a novel Graph Neural Network-Graph Multi-Dimensional Feature Network (GMDFN). The method is accomplished by mining feature information from diverse dimensions and aggregating information using various strategies. Furthermore, a self-supervised learning module is built to keep the node feature information from being destroyed too much in the aggregation process to avoid over-smoothing. A large number of experiments on different real-world datasets have shown that the model outperforms various current GNN models and is more robust.
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Acknowledgements
This work is supported in part by the National Natural Science Foundation of China (12361072), Xinjiang Natural Science Foundation (2021D01C078).
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Yao, M., Yu, H., Bian, H. (2024). Graph Multi-dimensional Feature Network. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1961. Springer, Singapore. https://doi.org/10.1007/978-981-99-8126-7_12
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DOI: https://doi.org/10.1007/978-981-99-8126-7_12
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