  In this paper, we study the problem of author identification under
double-blind review setting, which is to identify potential authors given
information of an anonymized paper. Different from existing approaches that
rely heavily on feature engineering, we propose to use network embedding
approach to address the problem, which can automatically represent nodes into
lower dimensional feature vectors. However, there are two major limitations in
recent studies on network embedding: (1) they are usually general-purpose
embedding methods, which are independent of the specific tasks; and (2) most of
these approaches can only deal with homogeneous networks, where the
heterogeneity of the network is ignored. Hence, challenges faced here are two
folds: (1) how to embed the network under the guidance of the author
identification task, and (2) how to select the best type of information due to
the heterogeneity of the network.
  To address the challenges, we propose a task-guided and path-augmented
heterogeneous network embedding model. In our model, nodes are first embedded
as vectors in latent feature space. Embeddings are then shared and jointly
trained according to task-specific and network-general objectives. We extend
the existing unsupervised network embedding to incorporate meta paths in
heterogeneous networks, and select paths according to the specific task. The
guidance from author identification task for network embedding is provided both
explicitly in joint training and implicitly during meta path selection. Our
experiments demonstrate that by using path-augmented network embedding with
task guidance, our model can obtain significantly better accuracy at
identifying the true authors comparing to existing methods.
