  Graph embedding algorithms are used to efficiently represent (encode) a graph
in a low-dimensional continuous vector space that preserves the most important
properties of the graph. One aspect that is often overlooked is whether the
graph is directed or not. Most studies ignore the directionality, so as to
learn high-quality representations optimized for node classification. On the
other hand, studies that capture directionality are usually effective on link
prediction but do not perform well on other tasks. This preliminary study
presents a novel text-enriched, direction-aware algorithm called DIAGRAM ,
based on a carefully designed multi-objective model to learn embeddings that
preserve the direction of edges, textual features and graph context of nodes.
As a result, our algorithm does not have to trade one property for another and
jointly learns high-quality representations for multiple network analysis
tasks. We empirically show that DIAGRAM significantly outperforms six
state-of-the-art baselines, both direction-aware and oblivious ones,on link
prediction and network reconstruction experiments using two popular datasets.
It also achieves a comparable performance on node classification experiments
against these baselines using the same datasets.
