  Data intensive research requires the support of appropriate datasets.
However, it is often time-consuming to discover usable datasets matching a
specific research topic. We formulate the dataset discovery problem on an
attributed heterogeneous graph, which is composed of paper-paper citation,
paper-dataset citation, and also paper content. We propose to characterize both
paper and dataset nodes by their commonly shared latent topics, rather than
learning user and item representations via canonical graph embedding models,
because the usage of datasets and the themes of research projects can be
understood on the common base of research topics. The relevant datasets to a
given research project can then be inferred in the shared topic space. The
experimental results show that our model can generate reasonable profiles for
datasets, and recommend proper datasets for a query, which represents a
research project linked with several papers.
