Influence Maximization (IM) is to identify the seed set to maximize information dissemination in a network. Elegant IM algorithms could naturally extend to cases where each node is equipped with a specific weight, reflecting individual gains to measure the node's importance. Prevailing literature typically assumes such individual gains remain constant throughout the cascade process and are solvable through explicit formulas based on the node's characteristics and network topology. However, this assumption is not always feasible for two reasons: 1)Unobservability: The individual gains of each node are primarily evaluated by the difference between the outputs in the activated and non-activated states. In practice, we can only observe one of these states, with the other remaining unobservable post-propagation. 2)Environmental sensitivity: In addition to the node's inherent properties, individual gains are also sensitive to the activation status of surrounding nodes, which is dynamic during iteration even when the network topology remains static. To address these challenges, we extend the consideration of IM to a broader scenario with dynamic node individual gains, leveraging causality techniques. In our paper, we introduce a Causal Influence Maximization (CauIM) framework and develop two algorithms, G-CauIM and A-CauIM, where the latter incorporates a novel acceleration technique. Theoretically, we establish the generalized lower bound of influence spread and provide robustness analysis. Empirically, in synthetic and real-world experiments, we demonstrate the effectiveness and robustness of our algorithms.

翻译：影响力最大化旨在识别种子集合以最大化网络中的信息传播。优秀的IM算法可自然扩展到每个节点配备特定权重的情形，该权重反映个体收益以衡量节点重要性。现有文献通常假设此类个体收益在级联过程中保持恒定，并可通过基于节点特征与网络拓扑的显式公式求解。然而，该假设因以下两点并不总是成立：1)不可观测性：节点个体收益主要通过激活态与非激活态的输出差异评估。实践中我们仅能观测其中一种状态，另一状态在传播后不可观测；2)环境敏感性：除节点固有属性外，个体收益还对周围节点的激活状态敏感，即使网络拓扑静态，该状态在迭代过程中仍动态变化。为应对这些挑战，我们借助因果推断技术将IM考量扩展至具有动态节点个体收益的更广泛场景。本文提出因果影响力最大化框架，并开发G-CauIM与A-CauIM两种算法，后者融合了新型加速技术。理论上，我们建立了影响力传播的广义下界并给出鲁棒性分析。通过合成数据与真实场景实验，我们验证了所提算法的有效性与鲁棒性。