Abstract
Network representation learning (NRL) aims at building a low-dimensional vector for each vertex in a network, which is also increasingly recognized as an important aspect for network analysis. Some current NRL methods only focus on learning representations using the network structure. However, vertices in lots of networks may contain community information or text contents, which could be good for relevant evaluation tasks, such as vertex classification, link prediction and so on. Since it has been proved that DeepWalk is actually equivalent to matrix factorization, we propose community and text-enhanced DeepWalk (CTDW) based on the inductive matrix completion algorithm, which incorporates community features and text features of vertices into NRL under the framework of matrix factorization. In experiments, we evaluate the proposed CTDW compared with other state-of-the-art methods on vertex classification. The experimental results demonstrate that CTDW outperforms other baseline methods on three real-world datasets.
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Acknowledgements
The work is supported by the National Natural Science Foundation of China under grant 11661069, grant 61663041 and grant 61763041, the Program for Changjiang Scholars and Innovative Research Team in Universities under grant IRT_15R40, the Key Laboratory of Tibetan Intelligent Information Processing and Machine Translation in Qinghai Normal University, the Research Funds for Chunhui Program of Ministry of Education of China under grant Z2014022, the Natural Science Foundation of Qinghai Province under grant 2013-Z-Y17 and grant 2014-ZJ-721, the Fundamental Research Funds for the Central Universities under grant 2017TS045.
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Zhu, Y., Ye, Z., Zhao, H., Zhang, K. (2018). Network Representation Learning Based on Community and Text Features. In: Sun, M., Liu, T., Wang, X., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. CCL NLP-NABD 2018 2018. Lecture Notes in Computer Science(), vol 11221. Springer, Cham. https://doi.org/10.1007/978-3-030-01716-3_29
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DOI: https://doi.org/10.1007/978-3-030-01716-3_29
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