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
utilize node features on graph-structured data showing superior performance.
However, popular GNN-based architectures operate on one homogeneous network.
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
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) in a hierarchical way. Then, GRAF processes a network fusion step
weighing each edge according to learned node- and association-level attention,
which results in a fused enriched network. 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 the node features on the graph-structured data for the
prediction task or any other downstream analysis. Our extensive evaluations of
prediction tasks from different domains showed that GRAF outperformed the
state-of-the-art methods. Utilization of learned node-level and
association-level attention allowed us to prioritize the edges properly. The
source code for our tool is publicly available at
https://github.com/bozdaglab/GRAF.Comment: 11 pages, 1 figur