Fusing multiplex heterogeneous networks using graph attention-aware fusion networks
Abstract Graph Neural Networks (GNN) emerged as a deep learning framework to generate node and graph embeddings for downstream machine learning tasks. Popular GNN-based architectures operate on networks of single node and edge type. However, a large number of real-world networks include multiple typ...
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| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-78555-4 |
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| author | Ziynet Nesibe Kesimoglu Serdar Bozdag |
| author_facet | Ziynet Nesibe Kesimoglu Serdar Bozdag |
| author_sort | Ziynet Nesibe Kesimoglu |
| collection | DOAJ |
| description | Abstract Graph Neural Networks (GNN) emerged as a deep learning framework to generate node and graph embeddings for downstream machine learning tasks. Popular GNN-based architectures operate on networks of single node and edge type. However, a large number of real-world networks include multiple types of nodes and edges. Enabling these architectures to work on networks with multiple node and edge types brings additional challenges due to the heterogeneity of the networks and the multiplicity of the existing associations. In this study, we present a framework, named GRAF (Graph Attention-aware Fusion Networks), to convert multiplex heterogeneous networks to homogeneous networks to make them more suitable for graph representation learning. Using attention-based neighborhood aggregation, GRAF learns the importance of each neighbor per node (called node-level attention) followed by the importance of each network layer (called network layer-level attention). Then, GRAF processes a network fusion step weighing each edge according to the learned attentions. After an edge elimination step based on edge weights, GRAF utilizes Graph Convolutional Networks (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 and state-of-the-art (SOTA) methods. We were able to interpret GRAF’s findings utilizing the attention weights. Source code for GRAF is publicly available at https://github.com/bozdaglab/GRAF . |
| format | Article |
| id | doaj-art-896caf108117458b9d06e650404052f2 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-896caf108117458b9d06e650404052f22025-08-20T02:08:24ZengNature PortfolioScientific Reports2045-23222024-11-0114111110.1038/s41598-024-78555-4Fusing multiplex heterogeneous networks using graph attention-aware fusion networksZiynet Nesibe Kesimoglu0Serdar Bozdag1Department of Computer Science and Engineering, University of North TexasDepartment of Computer Science and Engineering, University of North TexasAbstract Graph Neural Networks (GNN) emerged as a deep learning framework to generate node and graph embeddings for downstream machine learning tasks. Popular GNN-based architectures operate on networks of single node and edge type. However, a large number of real-world networks include multiple types of nodes and edges. Enabling these architectures to work on networks with multiple node and edge types brings additional challenges due to the heterogeneity of the networks and the multiplicity of the existing associations. In this study, we present a framework, named GRAF (Graph Attention-aware Fusion Networks), to convert multiplex heterogeneous networks to homogeneous networks to make them more suitable for graph representation learning. Using attention-based neighborhood aggregation, GRAF learns the importance of each neighbor per node (called node-level attention) followed by the importance of each network layer (called network layer-level attention). Then, GRAF processes a network fusion step weighing each edge according to the learned attentions. After an edge elimination step based on edge weights, GRAF utilizes Graph Convolutional Networks (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 and state-of-the-art (SOTA) methods. We were able to interpret GRAF’s findings utilizing the attention weights. Source code for GRAF is publicly available at https://github.com/bozdaglab/GRAF .https://doi.org/10.1038/s41598-024-78555-4Attention aware network fusionGraph neural networksDrug ADR prediction |
| spellingShingle | Ziynet Nesibe Kesimoglu Serdar Bozdag Fusing multiplex heterogeneous networks using graph attention-aware fusion networks Scientific Reports Attention aware network fusion Graph neural networks Drug ADR prediction |
| title | Fusing multiplex heterogeneous networks using graph attention-aware fusion networks |
| title_full | Fusing multiplex heterogeneous networks using graph attention-aware fusion networks |
| title_fullStr | Fusing multiplex heterogeneous networks using graph attention-aware fusion networks |
| title_full_unstemmed | Fusing multiplex heterogeneous networks using graph attention-aware fusion networks |
| title_short | Fusing multiplex heterogeneous networks using graph attention-aware fusion networks |
| title_sort | fusing multiplex heterogeneous networks using graph attention aware fusion networks |
| topic | Attention aware network fusion Graph neural networks Drug ADR prediction |
| url | https://doi.org/10.1038/s41598-024-78555-4 |
| work_keys_str_mv | AT ziynetnesibekesimoglu fusingmultiplexheterogeneousnetworksusinggraphattentionawarefusionnetworks AT serdarbozdag fusingmultiplexheterogeneousnetworksusinggraphattentionawarefusionnetworks |