Abstract
In this paper, we propose a predictive network representation learning (PNRL) model to solve the structural link prediction problem. The proposed model de-nes two learning objectives, i.e., observed structure preservation and hidden link prediction. To integrate the two objectives in a unified model, we develop an e-ective 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.
Original language | English |
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Title of host publication | SIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval |
Publisher | Association for Computing Machinery, Inc |
Pages | 969-972 |
Number of pages | 4 |
ISBN (Electronic) | 9781450350228 |
DOIs | |
Publication status | Published - 7 Aug 2017 |
Event | 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017 - Tokyo, Shinjuku, Japan Duration: 7 Aug 2017 → 11 Aug 2017 |
Conference
Conference | 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017 |
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Country/Territory | Japan |
City | Tokyo, Shinjuku |
Period | 7/08/17 → 11/08/17 |
Keywords
- Network Representation Learning; Link Prediction
ASJC Scopus subject areas
- Information Systems
- Software
- Computer Graphics and Computer-Aided Design