GBNSS: A Method Based on Graph Neural Networks (GNNs) for Global Biological Network Similarity Search  
[PERSON]  
1 School of Computer and Electronics Information, Guangxi University, Nanning 530004, China 1  
[PERSON]  
1 School of Computer and Electronics Information, Guangxi University, Nanning 530004, China 12 Guangqi Key Laboratory of Multimedia Communications Network Technology, Nanning 530004, China 23 Tianjin Key Laboratory of Biosensing and Molecular Recognition, College of Chemistry, Nankai University, Tianjin 300071, China 3  
[PERSON]  
3 Tianjin Key Laboratory of Biosensing and Molecular Recognition, College of Chemistry, Nankai University, Tianjin 300071, China 3  
[PERSON]  
1 School of Computer and Electronics Information, Guangxi University, Nanning 530004, China 12 Guangqi Key Laboratory of Multimedia Communications Network Technology, Nanning 530004, China 2  
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methods insist that the search results must match a subgraph of the original query network, focusing on graph isomorphism--an NP-complete problem [15] that becomes impractical as network size increases. In contrast, approximate query methods are more flexible, prioritizing similarity in network topology or biological characteristics. These methods are particularly useful for accommodating human error and enhancing computational speed. Existing biological similarity search methods focus on reducing query time and improving query accuracy. These methods to minimize query time can be categorized into building indexes and leveraging parallel computing resources. For instance, [16; 17; 18] reduce query time by creating a biological database network index and minimizing network size. Similarly, methods [19; 20] enhance efficiency by utilizing CPU computing resources through parallelism. Methods to improve query accuracy are generally classified into three categories: those based on biological network topology, those based on biological sequence similarity, and those combining both. For example, [21; 22; 23; 24] use network topology such as important nodes or converting network topology to trees, while [25; 26; 27] leverage biological sequence similarity for biochemical accuracy. Methods like [28; 29; 30] combine subgraph signature and sequence similarity through model training for more accurate results. Despite progress, existing methods for biological network similarity search have notable drawbacks. Index-based query methods are limited by the topology of the network. Parallel computing approaches, while effective, are limited by the number of network nodes and struggle with networks exceeding 100 nodes. Moreover, methods leveraging topological and biological information enhance accuracy but face significant increases in query time with larger networks. To address these issues, we propose GBNSS (graph neural networks for biological network similarity search), a graph neural network-based query algorithm for biological networks. GBNSS is a topology-free method capable of handling biological networks with arbitrary structures. GBNSS extracts topological features and biochemical information from nodes in a biological network, builds a feature representation for each network, and fully captures the network's information by aggregating neighboring nodes' features and assigning higher weights to important nodes. Self-supervised training was performed using the biological network alignment metrics EC (Edge Correctness), LCCS (Largest Common Connected Component), S3 (Symmetric substructure score), and GOSim (GO annotation similarity) as training labels. The proposed scheme has been tested across all available biological networks for four different species, showing that it performs competitively when compared to the latest methods. Our contributions are as follows:  
1.