## 1 Introduction  
At present, various complex network structures have been integrated into our life, such as transportation networks, computer networks, citation networks, and so on. Thus, emerging network science has become an important research field. In a complex network, there is a type of node that plays a key role in the information dissemination between local network structures, which is called a bridge or bridge node. In this paper, we use bridge node to refer to such a node. Due to their special topological position in the network, when these bridging nodes are activated, they can effectively promote the information flow between local structures in the network; on the contrary, when immune to them, they can effectively prevent the information flow between local structures. Accurately discovering bridge nodes in the network is an important research topic in network science, and its results have important application value in scenarios such as community immunization [1,2] and drug analysis [3]. So far, researchers have proposed many detection methods for bridge nodes, which are mainly divided into two categories: methods based on community structure and methods that do not consider community structure. The method based on community structure focuses on the detection of bridge nodes as important information transmission media between communities, which is more in line with the definition of \"bridge\"; the method of bridge node detection that does not depend on community structure focuses on the bridging role of nodes in the global network; this \"bridging effect\" is more similar to the definition of node \"influence\". On the other hand, the method based on community structure is more suitable for some real-world application scenarios. Suppose a large computer cluster network is infected by a virus, and it is necessary to immunize the computers in key locations to prevent the further spread of the virus. If the location of the bridge nodes is determined based on the network community structure, the original network topology will be protected to the greatest extent from damage after the node computer is removed, and most functions of the network will be maintained normally. Therefore, in recent years, more and more bridge node-related work is based on the community structure. As far as we know, the current community-based algorithms for detecting bridge nodes are based on the local structure information of nodes and perform index calculations to define the bridging role of nodes. The calculation of a single index means that such methods cannot comprehensively consider the multi-dimensional information of nodes to characterize the bridging effect of nodes, thus affecting the accuracy of evaluation results. In addition, community-based structures may rely on community detection algorithms, resulting in the consumption of additional computing resources. This paper systematically studies bridge nodes in complex networks and proposes a deep learning-based method for detecting bridge nodes between communities. The paper contains the following three main contributions:  
* A deep learning-based framework named BND is proposed to detect bridge nodes, through which we can avoid expensive community detection algorithms;
* On this basis, we applied graph learning technology and constructed a GNN model, BND-GCN, for bridge node detection on complex networks;
* We test our model on sex real social networks and compare it with other baseline methods. Experiments show that BND-GCN performs well on bridge node detection tasks, and is generally better than the baselines. The rest of this paper is organized as follows. In Section 2, we systematically introduce a series of related works on bridge node detection and graph representation learning. Section 3 details our proposed bridge node detection framework, BND. In Section 4, a method for constructing the training dataset is presented. In Section 5, extensive experiments are designed and conducted to verify the effectiveness of the framework.