  In recent time, applications of network embedding in mining real-world
information network have been widely reported in the literature. Majority of
the information networks are heterogeneous in nature. Meta-path is one of the
popularly used approaches for generating embedding in heterogeneous networks.
As meta-path guides the models towards a specific sub-structure, it tends to
lose some hetero- geneous characteristics inherently present in the underlying
network. In this paper, we systematically study the effects of different
meta-paths using different state-of-art network embedding methods
(Metapath2vec, Node2vec, and VERSE) over DBLP bibliographic network and
evaluate the performance of embeddings using two applications (co-authorship
prediction and authors research area classification tasks). From various
experimental observations, it is evident that embedding using different
meta-paths perform differently over different tasks. It shows that meta- paths
are task-dependent and can not be generalized for different tasks. We further
observe that embedding obtained after considering all the node and relation
types in bibliographic network outperforms its meta- path based counterparts.
