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Name disambiguation aims to identify unique authors with the same name.Existing name disambiguation methods always exploit author attributes toenhance disambiguation results. However, some discriminative author attributes(e.g., email and affiliation) may change because of graduation or job-hopping,which will result in the separation of the same author's papers in digitallibraries. Although these attributes may change, an author's co-authors andresearch topics do not change frequently with time, which means that paperswithin a period have similar text and relation information in the academicnetwork. Inspired by this idea, we introduce Multi-view Attention-basedPairwise Recurrent Neural Network (MA-PairRNN) to solve the name disambiguationproblem. We divided papers into small blocks based on discriminative authorattributes and blocks of the same author will be merged according to pairwiseclassification results of MA-PairRNN. MA-PairRNN combines heterogeneous graphembedding learning and pairwise similarity learning into a framework. Inaddition to attribute and structure information, MA-PairRNN also exploitssemantic information by meta-path and generates node representation in aninductive way, which is scalable to large graphs. Furthermore, a semantic-levelattention mechanism is adopted to fuse multiple meta-path basedrepresentations. A Pseudo-Siamese network consisting of two RNNs takes twopaper sequences in publication time order as input and outputs theirsimilarity. Results on two real-world datasets demonstrate that our frameworkhas a significant and consistent improvement of performance on the namedisambiguation task. It was also demonstrated that MA-PairRNN can perform wellwith a small amount of training data and have better generalization abilityacross different research areas.

Pairwise Learning for Name Disambiguation in Large-Scale Heterogeneous Academic Networks