<ul><li>Accurate representation to an academic network is of great significance to academic relationship mining like predicting scientific impact.</li><li>The paper proposes a Prediction-sampling-based Latent Factorization of Tensors (PLFT) model to address the issue of high-dimensional and incomplete academic networks.</li><li>The PLFT model includes a cascade LFT architecture to enhance model representation learning ability and a predicting-sampling strategy to more accurately learn the network representation.</li><li>Experimental results show that the PLFT model outperforms existing models in predicting unexplored relationships in academic networks.</li></ul>

Academic Network Representation via Prediction-Sampling Incorporated Tensor Factorization

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