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QPGCN: graph convolutional network with a quadratic polynomial filter for overcoming over-smoothing

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Abstract

Graph convolutional networks have developed rapidly these years. Over-smoothing is an important factor that makes it difficult to deepen the networks, affecting the further development of graph convolutional networks. There have been some studies to solve the over-smoothing issue. However, the shapes of the filters in the existing graph convolutional networks are fixed for various data, and the convolution operations in some networks require eigendecomposition. Starting from the relationship between filters and over-smoothing, we propose a novel way to overcome over-smoothing in this paper. In this way, a novel quadratic polynomial filter (QPF) is proposed, and then a quadratic polynomial graph convolutional network (QPGCN) is derived. Without increasing the complexity of the network, QPGCN can adaptively learn the shape of QPF and does not require eigendecomposition, which can better alleviate over-smoothing. The extensive experiments on Cora, Citeseer, Pubmed, DBLP and Ogbn-arxiv datasets show that QPGCN achieves state-of-the-art performance.

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Notes

  1. If all f(λi) = 1,i ∈ [N], \(\text {diag}\left (F(\vec {\lambda }) \right ) = I_{N}\) and the convolution operation will be \(F \star _{\mathcal {G}} \boldsymbol {x} = U\left [ I_{N} \right ] U^{T}\boldsymbol {x} = \boldsymbol {x} \).

  2. Since GCN employed an augmented adjacency matrix, an approximate filter \(F_{GCN}(\vec {\lambda } ) \approx \vec {1}-\bar {d}/\left (\bar {d}+1 \right ) \vec {\lambda }\) was used [1], where \(\bar {d}\) is the average degree.

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Acknowledgments

This study was supported by Science Research Project of Liaoning Department of Education of China (No. LJKZ0008) and Science and Technology Development Project of Liaoning Province of China (No. 2021JH6/10500127).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Chuangxin Road, Shenyang, 110169, Liaoning, China

    Gongce Wu, Shukuan Lin, Xiaoxue Shao, Peng Zhang & Jianzhong Qiao

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  1. Gongce Wu
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Correspondence to Shukuan Lin.

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Wu, G., Lin, S., Shao, X. et al. QPGCN: graph convolutional network with a quadratic polynomial filter for overcoming over-smoothing. Appl Intell 53, 7216–7231 (2023). https://doi.org/10.1007/s10489-022-03836-2

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