Finding Key Nodes in Complex Networks via Deep Reinforcement Learning and Multi Attention Node Connectivity

Identifying and safeguarding key nodes is crucial for maintaining the reliability and stability of business functions. Currently, most key node identification algorithms are based on manual or deductive models, which are complex and suboptimal. While deep reinforcement learning algorithms have demonstrated promising practical results, they often exhibit excessive randomness in network feature extraction. To address this challenge, this paper proposes a deep reinforcement learning framework based on multi attributes attention mechanism. This framework extends node attributes to encompass general business scenarios and employs graph convolutional networks combined with an attention mechanism to learn adaptive weights for different attributes of various nodes. Subsequently, reinforcement learning is utilized to determine the sequence of key nodes in the network. This algorithm is compared with six algorithms across six types of networks, consistently achieving optimal results, thereby validating the effectiveness of the proposed approach.