Abstract
This work studies the problem of signed network embedding, which aims to obtain low-dimensional vectors for nodes in signed networks. Existing works mostly focus on learning representations via characterizing the social structural balance theory in signed networks. However, structural balance theory could not well satisfy some of the fundamental phenomena in real-world signed networks such as the direction of links. As a result, in this paper we integrate another theory Status Theory into signed network embedding since status theory can better explain the social mechanisms of signed networks. To be specific, we characterize the status of nodes in the semantic vector space and well design different ranking objectives for positive and negative links respectively. Besides, we utilize graph attention to assemble the information of neighborhoods. We conduct extensive experiments on three real-world datasets and the results show that our model can achieve a significant improvement compared with baselines.
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References
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, pp. 2787–2795 (2013)
Cygan, M., Pilipczuk, M., Pilipczuk, M., Wojtaszczyk, J.O.: Sitting closer to friends than enemies, revisited. In: Rovan, B., Sassone, V., Widmayer, P. (eds.) MFCS 2012. LNCS, vol. 7464, pp. 296–307. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32589-2_28
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)
Guha, R., Kumar, R., Raghavan, P., Tomkins, A.: Propagation of trust and distrust. In: Proceedings of the 13th International Conference on World Wide Web, pp. 403–412. ACM (2004)
Heider, F.: Attitudes and cognitive organization. J. Psychol. 21(1), 107–112 (1946)
Islam, M.R., Aditya Prakash, B., Ramakrishnan, N.: SIGNet: scalable embeddings for signed networks. In: Phung, D., Tseng, V.S., Webb, G.I., Ho, B., Ganji, M., Rashidi, L. (eds.) PAKDD 2018. LNCS (LNAI), vol. 10938, pp. 157–169. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93037-4_13
Kim, J., Park, H., Lee, J.E., Kang, U.: Side: representation learning in signed directed networks. In: Proceedings of the 2018 World Wide Web Conference on World Wide Web, pp. 509–518. International World Wide Web Conferences Steering Committee (2018)
Lee, J.B., Rossi, R.A., Kim, S., Ahmed, N.K., Koh, E.: Attention models in graphs: A survey. arXiv preprint arXiv:1807.07984 (2018)
Leskovec, J., Huttenlocher, D., Kleinberg, J.: Signed networks in social media. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1361–1370. ACM (2010)
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)
Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., Eliassi-Rad, T.: Collective classification in network data. AI Mag. 29(3), 93 (2008)
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)
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)
Wang, S., Tang, J., Aggarwal, C., Chang, Y., Liu, H.: Signed network embedding in social media. In: Proceedings of the 2017 SIAM International Conference on Data Mining, pp. 327–335. SIAM (2017)
Wang, S., Tang, J., Aggarwal, C., Liu, H.: Linked document embedding for classification. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 115–124. ACM (2016)
Wang, X., Cui, P., Wang, J., Pei, J., Zhu, W., Yang, S.: Community preserving network embedding. In: AAAI, pp. 203–209 (2017)
Xu, H., Liu, H., Wang, W., Sun, Y., Jiao, P.: NE-FLGC: network embedding based on fusing local (first-order) and global (second-order) network structure with node content. In: Phung, D., Tseng, V.S., Webb, G.I., Ho, B., Ganji, M., Rashidi, L. (eds.) PAKDD 2018. LNCS (LNAI), vol. 10938, pp. 260–271. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93037-4_21
Yuan, S., Wu, X., Xiang, Y.: SNE: signed network embedding. In: Kim, J., Shim, K., Cao, L., Lee, J.-G., Lin, X., Moon, Y.-S. (eds.) PAKDD 2017. LNCS (LNAI), vol. 10235, pp. 183–195. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57529-2_15
Zeiler, M.D.: Adadelta: an adaptive learning rate method. arXiv preprint arXiv:1212.5701 (2012)
Zheng, Q., Skillicorn, D.B.: Spectral embedding of signed networks. In: Proceedings of the 2015 SIAM International Conference on Data Mining, pp. 55–63. SIAM (2015)
Acknowledgments
This work was supported by the National Social Science Foundation Project (15BTQ056), the National Key R&D Program of China (2018YFC0809800, 2016QY15Z2502-02, 2018YFC0831000), the National Natural Science Foundation of China (91746205, 91746107, 51438009), and the Applied Basic Research Project of Qinghai Province (No: 2018-ZJ-707).
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Lu, C., Jiao, P., Liu, H., Wang, Y., Xu, H., Wang, W. (2019). SSNE: Status Signed Network Embedding. In: Yang, Q., Zhou, ZH., Gong, Z., Zhang, ML., Huang, SJ. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11441. Springer, Cham. https://doi.org/10.1007/978-3-030-16142-2_7
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