Abstract
Network embedding aims at learning the low dimensional representation of nodes. These representations can be widely used for network mining tasks, such as link prediction, anomaly detection, and classification. Recently, a great deal of meaningful research work has been carried out on this emerging network analysis paradigm. The real-world network contains different size clusters because of the edges with different relationship types. These clusters also reflect some features of nodes, which can contribute to the optimization of the feature representation of nodes. However, existing network embedding methods do not distinguish these relationship types. In this paper, we propose an unsupervised network representation learning model that can encode edge relationship information. Firstly, an objective function is defined, which can learn the edge vectors by implicit clustering. Then, a biased random walk is designed to generate a series of node sequences, which are put into Skip-Gram to learn the low dimensional node representations. Extensive experiments are conducted on several network datasets. Compared with the state-of-art baselines, the proposed method is able to achieve favorable and stable results in multi-label classification and link prediction tasks.
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Notes
- 1.
The alias sampling algorithm [12] method can be used to complete the sampling process in the time complexity of O(1).
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Acknowledgements
We are grateful to the anonymous reviewers for their valuable comments on this manuscript. The research has been supported by the National Key Research and Development Program of China under Grant 2017YFB1402400, in part by the Frontier and Application Foundation Research Program of CQ CSTC under Grant cstc2017jcyjAX0340, in part by the Key Industries Common Key Technologies Innovation Projects of CQ CSTC under Grant cstc2017zdcy-zdyxx0047, in part by the Chongqing Technological Innovation and Application Demonstration Project under Grant cstc2018jszx-cyzdX0086, in part by the Social Undertakings and Livelihood Security Science and Technology Innovation Fund of CQ CSTC under Grant cstc2017shmsA0641, in part by the Fundamental Research Funds for the Central Universities under Grant 2018CDYJSY0055, and in part by the Graduate Research and Innovation Foundation of Chongqing under Grant CYB18058.
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Li, Q., Zhong, J., Li, Q., Cao, Z., Wang, C. (2019). Enhancing Network Embedding with Implicit Clustering. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11446. Springer, Cham. https://doi.org/10.1007/978-3-030-18576-3_27
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