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Abstract: Success has been obtained using a semi-supervised graph analysis method based on a graph convolutional network (GCN). However, GCN ignores some local information at each node in the graph, so that data preprocessing is incomplete and the model generated is not accurate enough. Thus, in the case of numerous unsupervised models based on graph embedding technology, local node information is important. In this paper, we apply a local analysis method based on the similar neighbor hypothesis to a GCN, and propose a local density definition; we call this method LDGCN. The LDGCN algorithm processes the input data of GCN in two methods, i.e., the unbalanced and balanced methods. Thus, the optimized input data contains detailed local node information, and then the model generated is accurate after training. We also introduce the implementation of the LDGCN algorithm through the principle of GCN, and use three mainstream datasets to verify the effectiveness of the LDGCN algorithm (i.e., the Cora, Citeseer, and Pubmed datasets). Finally, we compare the performances of several mainstream graph analysis algorithms with that of the LDGCN algorithm. Experimental results show that the LDGCN algorithm has better performance in node classification tasks.

一个基于图卷积神经网络的局部密度优化方法

[1]Chen HC, Perozzi B, Al-Rfou R, et al., 2018. A tutorial on network embeddings. https://arxiv.org/abs/1808.02590v1

[2]Fouss F, Pirotte A, Renders JM, et al., 2007. Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Trans Knowl Data Eng, 19(3):355-369.

[3]Grover A, Leskovec J, 2016. node2vec: scalable feature learning for networks. Proc 22^nd ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.855-864.

[4]Grover A, Zweig A, Ermon S, 2018. Graphite: iterative generative modeling of graphs. https://arxiv.org/abs/1803.10459v2

[5]Kipf TN, Welling M, 2016. Semi-supervised classification with graph convolutional networks. https://arxiv.org/abs/1609.02907

[6]Langville AN, Meyer CD, 2006. Google’s PageRank and Beyond: the Science of Search Engine Rankings. Princeton University Press, Princeton, USA, p.234.

[7]Le Q, Mikolov T, 2014. Distributed representations of sentences and documents. Proc 31^st Int Conf on Machine Learning, p.II-1188-II-1196.

[8]LeCun Y, Boser B, Denker JS, et al., 1989. Backpropagation applied to handwritten zip code recognition. Neur Comput, 1(4):541-551.

[9]LeCun Y, Bottou L, Bengio Y, et al., 1998. Gradient-based learning applied to document recognition. Proc IEEE, 86(11):2278-2324.

[10]LeCun Y, Bottou Y, Hinton G, 2015. Deep learning. Nature, 521(7553):436-444.

[11]Lin F, Cohen WW, 2010. Semi-supervised classification of network data using very few labels. Int Conf on Advances in Social Networks Analysis and Mining, p.192-199.

[12]Lorrain F, White HC, 1971. Structural equivalence of individuals in social networks. J Math Soc, 1(1):49-80.

[13]Mikolov T, Chen K, Corrado G, et al., 2013. Efficient estimation of word representations in vector space. https://arxiv.org/abs/1301.3781

[14]Mnih A, Kavukcuoglu K, 2013. Learning word embeddings efficiently with noise-contrastive estimation. Proc 26^th Int Conf on Neural Information Processing Systems, p.2265-2273.

[15]Narayanan A, Chandramohan M, Chen LH, et al., 2016. subgraph2vec: learning distributed representations of rooted sub-graphs from large graphs. https://arxiv.org/abs/1606.08928

[16]Niepert M, Ahmed M, Kutzkov K, 2016. Learning convolutional neural networks for graphs. Proc 33^rd Int Conf on Machine Learning, p.2014-2023.

[17]Page L, Brin S, Motwani R, et al., 1998. The Pagerank Citation Ranking: Bringing Order to the Web. Technical Report SIDL-WP-1999-0120, Stanford InfoLab, Stanford, USA.

[18]Perozzi B, Al-Rfou R, Skiena S, 2014. DeepWalk: online learning of social representations. Proc 20^th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.701-710.

[19]Pizarro N, 2007. Structural identity and equivalence of individuals in social networks: beyond duality. Int Soc, 22(6):767-792.

[20]Ribeiro LFR, Saverese PHP, Figueiredo DR, 2017. struc2vec: learning node representations from structural identity. Proc 23^rd ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.385-394.

[21]Shuman DI, Narang SK, Frossard P, et al., 2013. The emerging field of signal processing on graphs: extending high- dimensional data analysis to networks and other irregular domains. IEEE Signal Process Mag, 30(3):83-98.

[22]Tang L, Liu H, 2011. Leveraging social media networks for classification. Data Min Knowl Discov, 23(3):447-478.

[23]Wang HF, Zhang CY, Lin DY, et al., 2019. An artificial intelligence based method for evaluating power grid node importance using network embedding and support vector regression. Front Inform Technol Electron Eng, 20(6): 816-828.

[24]Weston J, Ratle F, Mobahi H, et al., 2012. Deep learning via semi-supervised embedding. In: Montavon G, Orr GB, Müller KR (Eds.), Neural Networks: Tricks of the Trade. Springer, Berlin, Heidelberg, p.639-655.

[25]Yang LM, Zhang W, Chen YF, 2015. Time-series prediction based on global fuzzy measure in social networks. Front Inform Technol Electron Eng, 16(10):805-816.

[26]Yang ZL, Cohen WW, Salakhutdinov R, 2016. Revisiting semi-supervised learning with graph embeddings. Proc 33^rd Int Conf on Machine Learning, p.40-48.