Abstract
The attributed network embedding aims to learn the latent low-dimensional representations of nodes, while preserving the neighborhood relationship of nodes in the network topology as well as the similarities of attribute features. In this paper, we propose a deep model based on the positive point-wise mutual information (PPMI) for attributed network embedding. In our model, attribute features are transformed into an attribute graph, such that attribute features and network topology can be handled in the same way. Then, we perform the random surfing and calculate the PPMI on the attribute/topology graph to effectively maintain the structural characteristics and the high-order proximity information. The node representations are learned by a shared Auto-Encoder. Besides, the local pairwise constraint is used in the shared Auto-Encoder to improve the quality of node representations. Extensive experimental results on four real-world networks show the superior performance of the proposed model over the 10 baselines.
Supported by the National Natural Science Foundation of China (61762090, ,62062066, 61966036, and 61662086), the Natural Science Foundation of Yunnan Province (2016FA026), the Program for Innovation Research Team (in Science and Technology) in University of Yunnan Province (IRTSTYN), and the National Social Science Foundation of China (18XZZ005).
K. Dong and T. Huang—Both authors have contributed equally to this work.
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Dong, K., Huang, T., Zhou, L., Wang, L., Chen, H. (2021). Deep Attributed Network Embedding Based on the PPMI. In: Jensen, C.S., et al. Database Systems for Advanced Applications. DASFAA 2021 International Workshops. DASFAA 2021. Lecture Notes in Computer Science(), vol 12680. Springer, Cham. https://doi.org/10.1007/978-3-030-73216-5_18
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