  Author disambiguation arises when different authors share the same name,
which is a critical task in digital libraries, such as DBLP, CiteULike,
CiteSeerX, etc. While the state-of-the-art methods have developed various paper
embedding-based methods performing in a top-down manner, they primarily focus
on the ego-network of a target name and overlook the low-quality collaborative
relations existed in the ego-network. Thus, these methods can be suboptimal for
disambiguating authors.
  In this paper, we model the author disambiguation as a collaboration network
reconstruction problem, and propose an incremental and unsupervised author
disambiguation method, namely IUAD, which performs in a bottom-up manner.
Initially, we build a stable collaboration network based on stable
collaborative relations. To further improve the recall, we build a
probabilistic generative model to reconstruct the complete collaboration
network. In addition, for newly published papers, we can incrementally judge
who publish them via only computing the posterior probabilities. We have
conducted extensive experiments on a large-scale DBLP dataset to evaluate IUAD.
The experimental results demonstrate that IUAD not only achieves the promising
performance, but also outperforms comparable baselines significantly. Codes are
available at https://github.com/papergitgit/IUAD.
