GRAF: Graph Attention-aware Fusion Networks

• URL: http://arxiv.org/abs/2303.16781v2
• Date: Thu, 17 Aug 2023 16:41:03 GMT
• Title: GRAF: Graph Attention-aware Fusion Networks
• Authors: Ziynet Nesibe Kesimoglu, Serdar Bozdag
• Abstract summary: 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. We present a computational approach named GRAF (Graph Attention-aware Fusion Networks) utilizing GNN-based approaches on multiple networks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 .

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