  Finding an expert plays a crucial role in driving successful collaborations
and speeding up high-quality research development and innovations. However, the
rapid growth of scientific publications and digital expertise data makes
identifying the right experts a challenging problem. Existing approaches for
finding experts given a topic can be categorised into information retrieval
techniques based on vector space models, document language models, and
graph-based models. In this paper, we propose $\textit{ExpFinder}$, a new
ensemble model for expert finding, that integrates a novel $N$-gram vector
space model, denoted as $n$VSM, and a graph-based model, denoted as
$\textit{$\mu$CO-HITS}$, that is a proposed variation of the CO-HITS algorithm.
The key of $n$VSM is to exploit recent inverse document frequency weighting
method for $N$-gram words and $\textit{ExpFinder}$ incorporates $n$VSM into
$\textit{$\mu$CO-HITS}$ to achieve expert finding. We comprehensively evaluate
$\textit{ExpFinder}$ on four different datasets from the academic domains in
comparison with six different expert finding models. The evaluation results
show that $\textit{ExpFinder}$ is a highly effective model for expert finding,
substantially outperforming all the compared models in 19% to 160.2%.
