Semi-supervised Graph Embedding Approach to Dynamic Link Prediction

  • Ryohei Hisano
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)


We propose a simple discrete-time semi-supervised graph embedding approach to link prediction in dynamic networks. The learned embedding reflects information from both the temporal and cross-sectional network structures, which is performed by defining the loss function as a weighted sum of the supervised loss from the past dynamics and the unsupervised loss of predicting the neighborhood context in the current network. Our model is also capable of learning different embeddings for both formation and dissolution dynamics. These key aspects contribute to the predictive performance of our model and we provide experiments with four real-world dynamic networks showing that our method is comparable to state of the art methods in link formation prediction and outperforms state-of-the-art baseline methods in link dissolution prediction.


Link prediction Link dissolution Semi-supervised learning 


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Social ICT centerUniversity of TokyoTokyoJapan

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