Node influence ranking is a key topic in complex networks,and plays an important role in identifying key nodes and measuring node influence.There has been much research exploring node influence based on complex networks,with deep learning shows great potential.However,existing convolutional neural networks(CNNs)and graph neural networks(GNNs)are often based on fixed dimensional features as input and cannot effectively distinguish between neighboring nodes,making them unsuita-ble for diverse complex networks.In order to solve these problems,a simple and effective node influence ranking model is pro-posed in this paper.In this model,the input sequence of nodes contains information about the nodes themselves and their neigh-bors,and the length of the input sequence can be dynamically adjusted according to the network to ensure that the model obtains sufficient information about the nodes.The model also uses the self-attention mechanism to enable nodes to efficiently aggregate information about their neighbors in the input sequence,thus identifying the influence of nodes.Experiments are conducted on 12 real network datasets to verify the effectiveness of the model against seven existing methods using multi-dimensional evaluation criteria.Experimental results show that the model can identify the influence of nodes in complex networks more effectively.
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