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2017 | OriginalPaper | Buchkapitel

SNE: Signed Network Embedding

verfasst von : Shuhan Yuan, Xintao Wu, Yang Xiang

Erschienen in: Advances in Knowledge Discovery and Data Mining

Verlag: Springer International Publishing

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Abstract

Several network embedding models have been developed for unsigned networks. However, these models based on skip-gram cannot be applied to signed networks because they can only deal with one type of link. In this paper, we present our signed network embedding model called SNE. Our SNE adopts the log-bilinear model, uses node representations of all nodes along a given path, and further incorporates two signed-type vectors to capture the positive or negative relationship of each edge along the path. We conduct two experiments, node classification and link prediction, on both directed and undirected signed networks and compare with four baselines including a matrix factorization method and three state-of-the-art unsigned network embedding models. The experimental results demonstrate the effectiveness of our signed network embedding.

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Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. TPAMI 35(8), 1798–1828 (2013)CrossRef

Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3, 1137–1155 (2003)MATH

Cao, S., Lu, W., Xu, Q.: Deep neural networks for learning graph representations. In: AAAI (2016)

Chang, S., Han, W., Tang, J., Qi, G.-J., Aggarwal, C.C., Huang, T.S.: Heterogeneous network embedding via deep architectures. In: KDD (2015)

Chiang, K.-Y., Hsieh, C.-J., Natarajan, N., Tewari, A., Dhillon, I.S.: Prediction, clustering in signed networks: a local to global perspective. arXiv:1302.5145 [cs] (2013)

Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)MATH

Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121–2159 (2011)MathSciNetMATH

Graves, A., Mohamed, A.-R., Hinton, G.: Speech recognition with deep recurrent neural networks. In: arXiv:1303.5778 [cs] (2013)

Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: KDD (2016)

10.

Heider, F.: Attitudes and cognitive organization. J. Psychol. 21(1), 107–112 (1946)CrossRef

11.

Jean, S., Cho, K., Memisevic, R., Bengio, Y.: On using very large target vocabulary for neural machine translation. arXiv:1412.2007 [cs] (2014)

12.

Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)

13.

Kumar, S., Spezzano, F., Subrahmanian, V.: VEWS: a Wikipedia vandal early warning system. In: KDD (2015)

14.

Kunegis, J., Schmidt, S., Lommatzsch, A., Lerner, J., De Luca, E.W., Albayrak, S.: Spectral analysis of signed graphs for clustering, prediction and visualization. In: SDM (2010)

15.

LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRef

16.

Leskovec, J., Huttenlocher, D., Kleinberg, J.: Signed networks in social media. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (2010)

17.

Li, J., Zhu, J., Zhang, B.: Discriminative deep random walk for network classification. In: ACL (2016)

18.

Li, Y., Wu, X., Lu, A.: On spectral analysis of directed signed graphs. CoRR, abs/1612.08102 (2016)

19.

Maaten, L.V.D., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)MATH

20.

Mikolov, T., Corrado, G., Chen, K., Dean, J.: Efficient estimation of word representations in vector space. In: ICLR (2013)

21.

Mnih, A., Hinton, G.: A scalable hierarchical distributed language model. In: NIPS (2008)

22.

Mnih, A., Kavukcuoglu, K.: Learning word embeddings efficiently with noise-contrastive estimation. In: NIPS (2013)

23.

Niepert, M., Ahmed, M., Kutzkov, K.: Learning convolutional neural networks for graphs. In: ICML (2016)

24.

Ou, M., Cui, P., Pei, J., Zhu, W.: Asymmetric transitivity preserving graph embedding. In: KDD (2016)

25.

Pan, S., Wu, J., Zhu, X., Zhang, C., Wang, Y.: Tri-party deep network representation. In: IJCAI (2016)

26.

Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: KDD (2014)

27.

Tang, J., Aggarwal, C., Liu, H.: Node classification in signed social networks. In: SDM (2016)

28.

Tang, J., Chang, Y., Aggarwal, C., Liu, H.: A survey of signed network mining in social media. arXiv:1511.07569 [physics] (2015)

29.

Tang, J., Qu, M., Mei, Q.: PTE: Predictive text embedding through large-scale heterogeneous text networks. In: KDD (2015)

30.

Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: large-scale information network embedding. In: WWW (2015)

31.

Tian, F., Gao, B., Cui, Q., Chen, E., Liu, T.-Y.: Learning deep representations for graph clustering. In: AAAI (2014)

32.

Tu, C., Zhang, W., Liu, Z., Sun, M.: Max-margin deepwalk: discriminative learning of network representation. In: IJCAI (2016)

33.

Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: KDD (2016)

34.

Wu, L., Wu, X., Lu, A., Li, Y.: On spectral analysis of signed and dispute graphs. In: ICDM, pp. 1049–1054 (2014)

35.

Wu, L., Ying, X., Wu, X., Lu, A., Zhou, Z.-H.: Spectral analysis of k-balanced signed graphs. In: Huang, J.Z., Cao, L., Srivastava, J. (eds.) PAKDD 2011. LNCS (LNAI), vol. 6635, pp. 1–12. Springer, Heidelberg (2011). doi:10.1007/978-3-642-20847-8_1 CrossRef

36.

Yanardag, P., Vishwanathan, S.: Deep graph kernels. In: KDD (2015)

37.

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

38.

Yang, Y., Lichtenwalter, R.N., Chawla, N.V.: Evaluating link prediction methods. Knowl. Inf. Syst. 45(3), 751–782 (2015)CrossRef

39.

Yang, Z., Cohen, W.W., Salakhutdinov, R.: Revisiting semi-supervised learning with graph embeddings. In: ICML (2016)

Titel: SNE: Signed Network Embedding
verfasst von: Shuhan Yuan
Xintao Wu
Yang Xiang
Verlag: Springer International Publishing
Buch: Advances in Knowledge Discovery and Data Mining
Print ISBN: 978-3-319-57528-5

Electronic ISBN: 978-3-319-57529-2

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-57529-2_15

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