Before revision:  We divided papers into small blocks based on discriminative author attributes and blocks of the same author will be merged according to pairwise classification results of  .  combines heterogeneous graph embedding learning and pairwise similarity learning into a framework.
After revision:  We divided papers into small blocks based on discriminative author attributes and blocks of the same author will be merged according to pairwise classification results of  . MA-PairRNN. MA-PairRNN combines heterogeneous graph embedding learning and pairwise similarity learning into a framework.