  Author name ambiguity causes inadequacy and inconvenience in academic
information retrieval, which raises the necessity of author name disambiguation
(AND). Existing AND methods can be divided into two categories: the models
focusing on content information to distinguish whether two papers are written
by the same author, the models focusing on relation information to represent
information as edges on the network and to quantify the similarity among
papers. However, the former requires adequate labeled samples and informative
negative samples, and are also ineffective in measuring the high-order
connections among papers, while the latter needs complicated feature
engineering or supervision to construct the network. We propose a novel
generative adversarial framework to grow the two categories of models together:
(i) the discriminative module distinguishes whether two papers are from the
same author, and (ii) the generative module selects possibly homogeneous papers
directly from the heterogeneous information network, which eliminates the
complicated feature engineering. In such a way, the discriminative module
guides the generative module to select homogeneous papers, and the generative
module generates high-quality negative samples to train the discriminative
module to make it aware of high-order connections among papers. Furthermore, a
self-training strategy for the discriminative module and a random walk based
generating algorithm are designed to make the training stable and efficient.
Extensive experiments on two real-world AND benchmarks demonstrate that our
model provides significant performance improvement over the state-of-the-art
methods.
