Robust representation learning for heterogeneous attributed networks

Fu, Yue; Yu, Xiaoli; Wu, Yongliang; Ding, Xueyi; Zhao, Shuliang

doi:10.1016/j.ins.2023.01.038

Cited by 10 publications

(1 citation statement)

References 15 publications

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“…Heterogeneous network representation learning has proved to be very useful in many downstream tasks [6] such as link prediction, node classification, node clustering, etc.…”

Section: Introductionmentioning

confidence: 99%

Robust and fast representation learning for heterogeneous information networks

Lei

Chen

et al. 2023

Front. Phys.

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Network representation learning is an important tool that can be used to optimize the speed and performance of downstream analysis tasks by extracting latent features of heterogeneous networks. However, in the face of new challenges of increasing network size, diverse latent features, and unseen network noise, existing representation models need to be further optimized. In this paper, a robust and fast representation learning model is proposed for heterogeneous networks, called RFRL. First, the global features of a heterogeneous network are divided into multiple intra-type local features and inter-type local features, and a type-aware biased sampling is designed to generate training samples for each local feature. Second, a node-type-aware and a link-type-aware shallow representation strategy are used to learn intra-type features and inter-type features respectively. This enables the model to achieve good performance while having high speed through the divide-and-conquer learning process and shallow learning model, thus coping with increasing network size and latent feature diversity. Finally, adversarial learning is used to integrate the above two representation strategies to address unseen network noise and enhance the robustness of representation learning. Extensive experiments on three network analysis tasks and three public datasets demonstrate the good performance of our RFRL model.

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“…Heterogeneous network representation learning has proved to be very useful in many downstream tasks [6] such as link prediction, node classification, node clustering, etc.…”

Section: Introductionmentioning

confidence: 99%