GRAF: Graph Attention-aware Fusion Networks

by   Ziynet Nesibe Kesimoglu, et al.

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


Label Aware Graph Convolutional Network -- Not All Edges Deserve Your Attention

Graph classification is practically important in many domains. To solve ...

Improving Fraud detection via Hierarchical Attention-based Graph Neural Network

Graph neural networks (GNN) have emerged as a powerful tool for fraud de...

Sparse Graph Attention Networks

Graph Neural Networks (GNNs) have proved to be an effective representati...

Bilinear Graph Neural Network with Neighbor Interactions

Graph Neural Network (GNN) is a powerful model to learn representations ...

Improving Article Classification with Edge-Heterogeneous Graph Neural Networks

Classifying research output into context-specific label taxonomies is a ...

Learning a Graph Neural Network with Cross Modality Interaction for Image Fusion

Infrared and visible image fusion has gradually proved to be a vital for...

How Attentive are Graph Attention Networks?

Graph Attention Networks (GATs) are one of the most popular GNN architec...

Please sign up or login with your details

Forgot password? Click here to reset