Abstract
Network representation learning (NRL), which has become an focus of current research, learns low-dimensional vertex representations to capture network information. However, conventional NRL models either largely neglect the rich semantic information on edges and fail to extract good features of relations, or employ complex models that have rather high space and time complexities. In this work, we present an efficient NRL model, MultNet, for Social Relation Extraction (SRE) task, which evaluates the ability of NRL models on modeling the relationships between vertices. We conduct extensive experiments on several public data sets and experiments on SRE indicate that MultNet outperforms other baseline models significantly.
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Acknowledgement
This work is supported by the National Key Research and Development Program of China (No. 2016YFB0800504), and National Natural Science Foundation of China (No. U163620068).
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Yuan, J., Gao, N., Wang, L., Liu, Z. (2018). MultNet: An Efficient Network Representation Learning for Large-Scale Social Relation Extraction. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11303. Springer, Cham. https://doi.org/10.1007/978-3-030-04182-3_45
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DOI: https://doi.org/10.1007/978-3-030-04182-3_45
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