  As science advances, the academic community has published millions of
research papers. Researchers devote time and effort to search relevant
manuscripts when writing a paper or simply to keep up with current research. In
this paper, we consider the problem of citation recommendation on graph and
propose a task-specific neighborhood construction strategy to learn the
distributed representations of papers. In addition, given the learned
representations, we investigate various schemes to rank the candidate papers
for citation recommendation. The experimental results show our proposed
neighborhood construction strategy outperforms the widely-used random walks
based sampling strategy on all ranking schemes, and the model based ranking
scheme outperforms embedding based rankings for both neighborhood construction
strategies. We also demonstrated that graph embedding is a robust approach for
citation recommendation when hidden ratio changes, while the performance of
classic methods drop significantly when the set of seed papers is becoming
small.
