Abstract
Learning low-dimensional embeddings of nodes in networks is an effective way to solve the network analytic problem, from traffic network to recommender systems. However, most existing approaches are inherently transductive, their framework is built on a single fixed graph. Inspired by node2vec, we optimize the random walk strategy and propose GVNP, an unsupervised method that can learn continuous feature representations for nodes and leverage node feature information to efficiently generate node embeddings for previously unseen data in networks. Experimental results demonstrate that GVNP performs well on the transductive and inductive task.
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Acknowledgement
This work was supported in part by the Natural Science Foundation of Guangdong (2019A1515010746, 2019A1515011549), in part by the SYSU Youth Teacher Development Program (19lgpy218), in part by the National Science Foundation of China (61972430).
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Zhang, D., Chen, Z., Zheng, P., Liu, H. (2021). GVNP: Global Vectors for Node Representation. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Advances in Artificial Intelligence and Security. ICAIS 2021. Communications in Computer and Information Science, vol 1422. Springer, Cham. https://doi.org/10.1007/978-3-030-78615-1_17
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DOI: https://doi.org/10.1007/978-3-030-78615-1_17
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