  Graph representation plays an important role in the field of financial risk
control, where the relationship among users can be constructed in a graph
manner. In practical scenarios, the relationships between nodes in risk control
tasks are bidirectional, e.g., merchants having both revenue and expense
behaviors. Graph neural networks designed for undirected graphs usually
aggregate discriminative node or edge representations with an attention
strategy, but cannot fully exploit the out-degree information when used for the
tasks built on directed graph, which leads to the problem of a directional
bias. To tackle this problem, we propose a Directed Graph ATtention network
called DGAT, which explicitly takes out-degree into attention calculation. In
addition to having directional requirements, the same node might have different
representations of its input and output, and thus we further propose a dual
embedding of DGAT, referred to as DEDGAT. Specifically, DEDGAT assigns
in-degree and out-degree representations to each node and uses these two
embeddings to calculate the attention weights of in-degree and out-degree
nodes, respectively. Experiments performed on the benchmark datasets show that
DGAT and DEDGAT obtain better classification performance compared to undirected
GAT. Also,the visualization results demonstrate that our methods can fully use
both in-degree and out-degree information.
