Abstract
Graph Neural Networks have experienced a rapid development in the last few years and become powerful tools for many machine learning tasks in graph domain. Graph Convolution Network is a breakthrough and become a strong baseline for node classification task. To this end, we perform a thorough experiment for several prominent GCN-related models, including GAT, AGNN, Co-Training GCN and Stochastic GCN. We found that different models take their advantages in different scenarios, depending on training set size, graph structure and datasets. Through our in-depth analysis of attention mechanism, dataset splits and the preprocessing for knowledge graphs, we report some interesting findings. And we look into GCNs for knowledge graphs carefully, then propose a new scheme for data processing, which achieves a better performance compared to traditional methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The NELL data we used can be found in https://github.com/thu-ml/stochastic_gcn/releases/download/0.1/nell.tar.gz.
References
Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: SIGMOD, pp. 1247–1250. ACM (2008)
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: NIPS, pp. 2787–2795 (2013)
Busbridge, D., Sherburn, D., Cavallo, P., Hammerla, N.Y.: Relational graph attention networks. arXiv preprint arXiv:1904.05811 (2019)
Chen, J., Zhu, J., Song, L.: Stochastic training of graph convolutional networks with variance reduction. In: ICML, pp. 941–949 (2018)
Gori, M., Monfardini, G., Scarselli, F.: A new model for learning in graph domains. In: IJCNN, vol. 2, pp. 729–734. IEEE (2005)
Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: NIPS, pp. 1024–1034 (2017)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2017)
Li, Q., Han, Z., Wu, X.M.: Deeper insights into graph convolutional networks for semi-supervised learning. In: AAAI (2018)
Li, Y., Tarlow, D., Brockschmidt, M., Zemel, R.: Gated graph sequence neural networks. arXiv preprint arXiv:1511.05493 (2015)
Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61–80 (2009)
Schlichtkrull, M., Kipf, T.N., Bloem, P., van den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 593–607. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93417-4_38
Thekumparampil, K.K., Wang, C., Oh, S., Li, L.J.: Attention-based graph neural network for semi-supervised learning. arXiv preprint arXiv:1803.03735 (2018)
Velikovi, P., Cucurull, G., Casanova, A., Romero, A., Lió, P., Bengio, Y.: Graph attention networks. In: ICLR (2018)
Xie, R., Liu, Z., Jia, J., Luan, H., Sun, M.: Representation learning of knowledge graphs with entity descriptions. In: AAAI (2016)
Yang, Z., Cohen, W., Salakhudinov, R.: Revisiting semi-supervised learning with graph embeddings. In: International Conference on Machine Learning, pp. 40–48 (2016)
Zhou, J., Cui, G., Zhang, Z., Yang, C., Liu, Z., Sun, M.: Graph neural networks: a review of methods and applications. arXiv preprint arXiv:1812.08434 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chen, Y., Hu, S., Zou, L. (2020). An In-depth Analysis of Graph Neural Networks for Semi-supervised Learning. In: Wang, X., Lisi, F., Xiao, G., Botoeva, E. (eds) Semantic Technology. JIST 2019. Communications in Computer and Information Science, vol 1157. Springer, Singapore. https://doi.org/10.1007/978-981-15-3412-6_7
Download citation
DOI: https://doi.org/10.1007/978-981-15-3412-6_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-3411-9
Online ISBN: 978-981-15-3412-6
eBook Packages: Computer ScienceComputer Science (R0)