Abstract
Network embedding is a method to learn low dimensional representation of nodes in large graph with the goal of capturing and preserving the network structure. Graph convolution networks (GCN) are successfully applied in node embedding task as they can learn sparse and discrete dependency in the data. Most of the existing work in GCN requires costly matrix operation. In this paper, we proposed a graph neighbor Sampling, Aggregation, and ATtention (GSAAT) framework. That does not need to know the graph structure upfront and avoid costly matrix operation. The proposed method first learn to aggregate the information of node’s neighbors and stacked a layer in which nodes are able to attend over the aggregated information of their neighbors feature. The proposed method achieved state-of-art performance in two classification benchmark: Cora, and Citeseer citation network dataset.
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Acknowledgements
Author like to thank Samadhan Engineering, Nepal for providing resources and suggestion for the improvement of result.
This work was supported by the Key Research and Development Program of Jiangsu Province (BE2019012).
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Gaudel, B., Guan, D., Yuan, W., Shrestha, D., Chen, B., Tu, Y. (2021). Graph Representation Learning Using Attention Network. In: Mei, H., et al. Big Data. BigData 2020. Communications in Computer and Information Science, vol 1320. Springer, Singapore. https://doi.org/10.1007/978-981-16-0705-9_10
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DOI: https://doi.org/10.1007/978-981-16-0705-9_10
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