The influence maximization problem that has drawn a great deal of attention from researchers aims to identify a subset of influential spreaders that can maximize the expected influence spread in social networks. Existing works on the problem primarily concentrate on developing non-adaptive policies, where all seeds will be ignited at the very beginning of the diffusion after the identification. However, in non-adaptive policies, budget redundancy could occur as a result of some seeds being naturally infected by other active seeds during the diffusion process. In this paper, the adaptive seeding policies are investigated for the intractable adaptive influence maximization problem. Based on deep learning model, a novel approach named graph convolutional networks with self-attention mechanism (ATGCN) is proposed to address the adaptive influence maximization as a regression task. A controlling parameter is introduced for the adaptive seeding model to make a tradeoff between the spreading delay and influence coverage. The proposed approach leverages the self-attention mechanism to dynamically assign importance weight to node representations efficiently to capture the node influence feature information relevant to the adaptive influence maximization problem. Finally, intensive experimental findings on six real-world social networks demonstrate the superiorities of the adaptive seeding policy over the state-of-the-art baseline methods to the conventional influence maximization problem. Meanwhile, the proposed adaptive seeding policy ATGCN improves the influence spread rate by up to 7% in comparison to the existing state-of-the-art greedy-based adaptive seeding policy.

影响力最大化问题引起了研究人员的广泛关注，旨在识别有影响力的传播者的子集，这些传播者可以最大化社交网络中的预期影响力传播。针对该问题的现有工作主要集中在制定非适应性政策，其中所有种子将在识别后扩散的一开始就被点燃。然而，在非适应性政策中，由于一些种子在传播过程中自然地被其他活跃种子感染，可能会出现预算冗余。在本文中，针对棘手的自适应影响最大化问题，研究了自适应播种策略。基于深度学习模型，提出了一种名为具有自注意力机制的图卷积网络（ATGCN）的新方法，将自适应影响最大化作为回归任务来解决。为自适应播种模型引入控制参数，以在传播延迟和影响覆盖范围之间进行权衡。所提出的方法利用自注意力机制有效地为节点表示动态分配重要性权重，以捕获与自适应影响最大化问题相关的节点影响特征信息。最后，对六个现实世界社交网络的深入实验结果证明了自适应播种策略相对于传统影响力最大化问题的最先进基线方法的优越性。同时，与现有最先进的基于贪婪的自适应播种策略相比，所提出的自适应播种策略 ATGCN 将影响力传播率提高了 7％。