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Star Topology Convolution for Graph Representation Learning
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  • Chong Wu ,
  • Zhenan Feng ,
  • Jiangbin Zheng ,
  • Houwang Zhang ,
  • Jiawang Cao ,
  • Hong YAN
Chong Wu
Department of Electrical Engineering and Centre for Intelligent Multidimensional Data Analysis, Department of Electrical Engineering, Department of Electrical Engineering, Department of Electrical Engineering, Department of Electrical Engineering, Department of Electrical Engineering

Corresponding Author:[email protected]

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Zhenan Feng
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Jiangbin Zheng
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Houwang Zhang
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Jiawang Cao
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Abstract

We present a novel graph convolutional method called star topology convolution (STC). This method makes graph convolution more similar to conventional convolutional in neural networks (CNNs) in Euclidean feature space. Unlike most existing spectral convolution methods, this method learns subgraphs which have a star topology rather than a fixed graph. It has fewer parameters in its convolution kernel and is inductive so that it is more flexible and can be applied to large and evolving graphs. As for CNNs in Euclidean feature spaces, the convolution kernel is localized and maintains good sharing. By increasing the depth of a layer, the method can learn lobal features like a CNN. To validate the method, STC was compared to state-of-the-art spectral convolution and spatial convolution methods in a supervised learning setting on three benchmark datasets: Cora, Citeseer and Pubmed. The experimental results show that STC outperforms the other methods. STC was also applied to protein identification tasks and outperformed traditional and advanced protein identification methods.
Dec 2022Published in Complex & Intelligent Systems volume 8 issue 6 on pages 5125-5141. 10.1007/s40747-022-00744-3