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2018 | OriginalPaper | Buchkapitel

NE-FLGC: Network Embedding Based on Fusing Local (First-Order) and Global (Second-Order) Network Structure with Node Content

verfasst von : Hongyan Xu, Hongtao Liu, Wenjun Wang, Yueheng Sun, Pengfei Jiao

Erschienen in: Advances in Knowledge Discovery and Data Mining

Verlag: Springer International Publishing

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Abstract

This paper studies the problem of Representation Learning for network with textual information, which aims to learn low dimensional vectors for nodes by leveraging network structure and textual information. Most existing works only focus on one aspect of network structure and cannot fuse network first-order proximity, second-order proximity and textual information. In this paper, we propose a novel network embedding method NE-FLGC: Network Embedding based on Fusing Local (first-order) and Global (second-order) network structure with node Content. Especially, we adopt context-enhance method that obtains node embedding by concatenating the vector of itself and the context vectors. In experiments, we compare our model with existing network embedding models on four real-world datasets. The experimental results demonstrate that NE-FLGC is stable and significantly outperforms state-of-the-art methods.

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Vorheriges Kapitel A Generalization of Recurrent Neural Networks for Graph Embedding

Nächstes Kapitel Category Multi-representation: A Unified Solution for Named Entity Recognition in Clinical Texts

Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Advances in Neural Information Processing Systems, pp. 585–591 (2002)

Cao, S., Lu, W., Xu, Q.: GraRep: learning graph representations with global structural information. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 891–900. ACM (2015)

Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864. ACM (2016)

Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759 (2016)

Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Stat. 22(1), 79–86 (1951)MathSciNetCrossRef

Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on Machine Learning, pp. 1188–1196 (2014)

Leskovec, J., Kleinberg, J., Faloutsos, C.: Graphs over time: densification laws, shrinking diameters and possible explanations. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 177–187. ACM (2005)

Levy, O., Goldberg, Y.: Neural word embedding as implicit matrix factorization. In: Advances in Neural Information Processing Systems, pp. 2177–2185 (2014)

Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Assoc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)CrossRef

10.

Lim, K.W., Buntine, W.: Bibliographic analysis with the citation network topic model. arXiv preprint arXiv:1609.06826 (2016)

11.

McCallum, A.K., Nigam, K., Rennie, J., Seymore, K.: Automating the construction of internet portals with machine learning. Inf. Retrieval 3(2), 127–163 (2000)CrossRef

12.

Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)

13.

Pan, S., Wu, J., Zhu, X., Zhang, C., Wang, Y.: Tri-party deep network representation. Network 11(9), 12 (2016)

14.

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)

15.

Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)CrossRef

16.

Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: LINE: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067–1077. International World Wide Web Conferences Steering Committee (2015)

17.

Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z.: Arnetminer: extraction and mining of academic social networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 990–998. ACM (2008)

18.

Tu, C., Liu, H., Liu, Z., Sun, M.: Cane: Context-aware network embedding for relation modeling. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol. 1, pp. 1722–1731 (2017)

19.

Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1225–1234. ACM (2016)

20.

Yang, C., Liu, Z., Zhao, D., Sun, M., Chang, E.Y.: Network representation learning with rich text information. In: IJCAI, pp. 2111–2117 (2015)

Titel: NE-FLGC: Network Embedding Based on Fusing Local (First-Order) and Global (Second-Order) Network Structure with Node Content
verfasst von: Hongyan Xu
Hongtao Liu
Wenjun Wang
Yueheng Sun
Pengfei Jiao
Verlag: Springer International Publishing
Buch: Advances in Knowledge Discovery and Data Mining
Print ISBN: 978-3-319-93036-7

Electronic ISBN: 978-3-319-93037-4

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-93037-4_21

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