Abstract
Graph convolution networks (GCN) have recently been one of the most powerful methods in various tasks such as node classification and graph clustering. In the present study, we propose RW-GCN which utilizes biased random walk to assist in feature aggregation and GCN training process. RW-GCN employs biased random walks to generate node pairs. These pairs can be utilized to build a symmetric matrix to replace the adjacent matrix for GCN training. With these pairs generated above, we train the latent representation vectors by skip-gram. Our experiments demonstrate that compared to GCN, our model generates better results on node classification tasks performed on multiple datasets. In this way, both homophily and structural equivalence can be considered. Results of experiments on three datasets are presented to prove the availability of our method.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China (No. 61662053) and the Natural Science Foundation of Inner Mongolia in China (Grant nos. 2018BS06001).
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Li, Y., Ban, Z. (2021). RW-GCN: Training Graph Convolution Networks with Biased Random Walk for Semi-supervised Classification. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12714. Springer, Cham. https://doi.org/10.1007/978-3-030-75768-7_6
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