  A large number of real-world networks include multiple types of nodes and
edges. Graph Neural Network (GNN) emerged as a deep learning framework to
generate node and graph embeddings for downstream machine learning tasks.
However, popular GNN-based architectures operate on single homogeneous
networks. Enabling them to work on multiple networks brings additional
challenges due to the heterogeneity of the networks and the multiplicity of the
existing associations. In this study, we present a computational approach named
GRAF (Graph Attention-aware Fusion Networks) utilizing GNN-based approaches on
multiple networks with the help of attention mechanisms and network fusion.
Using attention-based neighborhood aggregation, GRAF learns the importance of
each neighbor per node (called node-level attention) followed by the importance
of association (called association-level attention). Then, GRAF processes a
network fusion step weighing each edge according to learned node- and
association-level attentions. Considering that the fused network could be a
highly dense network with many weak edges depending on the given input
networks, we included an edge elimination step with respect to edges' weights.
Finally, GRAF utilizes Graph Convolutional Network (GCN) on the fused network
and incorporates node features on graph-structured data for a node
classification or a similar downstream task. To demonstrate GRAF's
generalizability, we applied it to four datasets from different domains and
observed that GRAF outperformed or was on par with the baselines,
state-of-the-art methods, and its own variations for each node classification
task. Source code for our tool is publicly available at
https://github.com/bozdaglab/GRAF .
