  Name disambiguation aims to identify unique authors with the same name.
Existing name disambiguation methods always exploit author attributes to
enhance 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 digital
libraries. Although these attributes may change, an author's co-authors and
research topics do not change frequently with time, which means that papers
within a period have similar text and relation information in the academic
network. Inspired by this idea, we introduce Multi-view Attention-based
Pairwise Recurrent Neural Network (MA-PairRNN) to solve the name disambiguation
problem. 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. In
addition to attribute and structure information, MA-PairRNN also exploits
semantic information by meta-path and generates node representation in an
inductive way, which is scalable to large graphs. Furthermore, a semantic-level
attention mechanism is adopted to fuse multiple meta-path based
representations. A Pseudo-Siamese network consisting of two RNNs takes two
paper sequences in publication time order as input and outputs their
similarity. Results on two real-world datasets demonstrate that our framework
has a significant and consistent improvement of performance on the name
disambiguation task. It was also demonstrated that MA-PairRNN can perform well
with a small amount of training data and have better generalization ability
across different research areas.
