Abstract
Artificial neural networks have proven to be one of the most effective algorithms for learning and predicting information from data. Data come in many formats depending on the source and the nature of the phenomenon they pertain to. Numerous neural network models were proposed to handle the different data formats as efficiently as possible. One of the more abstract data format is graph structured data. We propose the Combined Graph Diffusion Embedding Network (CGDEN). The model combines two diffusion models to exploit the features of the graph nodes as well as the underlying structure of the graph. The model’s performance was test in a node classification problem in semi-supervised setting, where only a fraction on the node labels were available in the training phase. Two benchmarking citation network datasets (Cora and Citeseer) were used to validate the model. The accuracy of the proposed model in node classification exceeded that of the previous models.
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
References
Srinivasan, A., Muggleton, S., King, R.D., Sternberg, M.J.E.: Mutagenesis: ILP experiments in a non-determinate biological domain. In: Proceedings of the 4th International Workshop on Inductive Logic Programming, vol. 237, pp. 217–232 (1994)
Baldi, P., Pollastri, G.: The principled design of large-scale recursive neural network architectures–DAG-RNNs and the protein structure prediction problem. J. Mach. Learn. Res. 4, 575–602 (2003)
Ohtsuki, H., Hauert, C., Lieberman, E., Nowak, M.A.: A simple rule for the evolution of cooperation on graphs and social networks. Nature 441, 502 (2006)
Redondi, A.E.C.: Radio map interpolation using graph signal processing. IEEE Commun. Lett. 22, 153–156 (2017)
Velickovic, P., et al.: Graph attention networks. arXiv preprint arXiv:1710.10903, January 2017
Bua, A., Gori, M., Santini, F.: Recursive neural networks applied to discourse representation theory. In: International Conference on Artificial Neural Networks, pp. 290–295 (2002)
Sandryhaila, A., Moura, J.M.F.: Discrete signal processing on graphs: frequency analysis. IEEE Trans. Sign. Process. 62, 3042–3054 (2014)
Yang, Z., Cohen, W.W., Salakhutdinov, R.: Revisiting semi-supervised learning with graph embeddings. arXiv preprint arXiv:1603.08861 (2016)
Donoser, M., Bischof, H.: Diffusion processes for retrieval revisited. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1320–1327 (2013). https://doi.org/10.1109/cvpr.2013.174
Zhou, L., Yang, Z., Yuan, Q., Zhou, Z., Hu, D.: Salient region detection via integrating diffusion-based compactness and local contrast. IEEE Trans. Image Process. 24, 3308–3320 (2015)
Gori, M., Monfardini, G., Scarselli, F.: A new model for learning in graph domains. In: Proceedings of the International Joint Conference on Neural Networks, vol. 2, pp. 729–734 (2005)
Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw./Publ. IEEE Neural Netw. Counc. 20, 61–80 (2009)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
Li, Y., Tarlow, D., Brockschmidt, M., Zemel, R.: Gated graph sequence neural networks. arXiv preprint arXiv:1511.05493 (2015)
Liao, R., et al.: Graph partition neural networks for semi-supervised classification. arXiv preprint arXiv:1803.06272 (2018)
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)
Bruna, J., Zaremba, W., Szlam, A., LeCun, Y.: Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203 (2013)
Henaff, M., Bruna, J., LeCun, Y.: Deep convolutional networks on graph-structured data. arXiv preprint arXiv:1506.05163 (2015)
Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in Neural Information Processing Systems, pp. 3844–3852 (2016)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Yadati, N., Nimishakavi, M., Yadav, P., Louis, A., Talukdar, P.: HyperGCN: hypergraph convolutional networks for semi-supervised classification. arXiv preprint arXiv:1809.02589 (2018)
Abu-El-Haija, S., Kapoor, A., Perozzi, B., Lee, J.: N-GCN: multi-scale graph convolution for semi-supervised node classification. arXiv preprint arXiv:1802.08888 (2018)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Vaswani, A., et al.: Attention is all you need. in Advances in Neural Information Processing Systems 5998–6008 (2017)
Zhang, J., et al.: GaAN: gated attention networks for learning on large and spatiotemporal graphs. arXiv preprint arXiv:1803.07294 (2018)
Zhu, X., Ghahramani, Z., Lafferty, J.D.: Semi-supervised learning using gaussian fields and harmonic functions. In: Proceedings of the 20th International Conference on Machine Learning (ICML-03), pp. 912–919 (2003)
Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710. ACM (2014)
Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J. Mach. Learn. Res. 7, 2399–2434 (2006)
Weston, J., Ratle, F., Mobahi, H., Collobert, R.: Deep learning via semi-supervised embedding. In: Neural Networks: Tricks of the Trade, pp. 639–655, Springer (2012)
Lu, Q., Getoor, L.: Link-based classification. In: Proceedings of the 20th International Conference on Machine Learning (ICML-03), pp. 496–503 (2003)
Goyal, P., Ferrara, E.: Graph embedding techniques, applications, and performance: a survey. Knowl.-Based Syst. 151, 78–94 (2018)
Cai, H., Zheng, V.W., Chang, K.: A comprehensive survey of graph embedding: problems, techniques and applications. IEEE Trans. Knowl. Data Eng. 30, 1616–1637 (2018)
Jiang, B., Lin, D., Tang, J.: Graph diffusion-embedding networks. arXiv preprint arXiv:1810.00797 (2018)
Sen, P., et al.: Collective classification in network data. AI Mag. 29, 93 (2008)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)
Clevert, D.-A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (ELUs). arXiv preprint arXiv:1511.07289 (2015)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Atwood, J., Towsley, D.: Diffusion-convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1993–2001 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Al-Gafri, A., Moinuddin, M., Al-Saggaf, U.M. (2020). Combining Diffusion Processes for Semi-supervised Learning on Graph Structured Data. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-29513-4_40
Download citation
DOI: https://doi.org/10.1007/978-3-030-29513-4_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29512-7
Online ISBN: 978-3-030-29513-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)