Zhitao Wang
Editorial Notes
A corrigendum was issued for this paper on September 22, 2021. You can download the corrigendum from the supplemental material section of this citation page.
ABSTRACT
In this paper, we propose a predictive network representation learning (PNRL) model to solve the structural link prediction problem. The proposed model defines two learning objectives, i.e., observed structure preservation and hidden link prediction. To integrate the two objectives in a unified model, we develop an effective sampling strategy to select certain edges in a given network as assumed hidden links and regard the rest network structure as observed when training the model. By jointly optimizing the two objectives, the model can not only enhance the predictive ability of node representations but also learn additional link prediction knowledge in the representation space. Experiments on four real-world datasets demonstrate the superiority of the proposed model over the other popular and state-of-the-art approaches.
Supplemental Material
Available for Download
Corrigendum to "Predictive Network Representation Learning for Link Prediction" by Wang et al., Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '17).
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Index Terms
- Predictive Network Representation Learning for Link Prediction
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