Title: Influence Maximization Considering Influence, Cost and Time Show Chinese title

Title: 考虑影响、成本和时间的影响最大化

Abstract: Influence maximization has been studied for social network analysis, such as viral marketing (advertising), rumor prevention, and opinion leader identification. However, most studies neglect the interplay between influence spread, cost efficiency, and temporal urgency. In practical scenarios such as viral marketing and information campaigns, jointly optimizing Influence, Cost, and Time is essential, yet remaining largely unaddressed in current literature. To bridge the gap, this paper proposes a new multi-objective influence maximization problem that simultaneously optimizes influence, cost, and time. We show the intuitive and empirical evidence to prove the feasibility and necessity of this multi-objective problem. We also develop an evolutionary variable-length search algorithm that can effectively search for optimal node combinations. The proposed EVEA algorithm outperforms all baselines, achieving up to 19.3% higher hypervolume and 25 to 40% faster convergence across four real-world networks, while maintaining a diverse and balanced Pareto front among influence, cost, and time objectives. Show Chinese abstract

Abstract: 影响最大化已被用于社交网络分析，例如病毒式营销（广告）、谣言预防和意见领袖识别。 然而，大多数研究忽视了影响扩散、成本效率和时间紧迫性之间的相互作用。 在病毒式营销和信息活动等实际场景中，联合优化影响、成本和时间是至关重要的，但在当前文献中仍基本未被解决。 为弥补这一差距，本文提出了一种新的多目标影响最大化问题，该问题同时优化影响、成本和时间。 我们展示了直观和实证证据，以证明这一多目标问题的可行性和必要性。 我们还开发了一种进化可变长度搜索算法，可以有效搜索最优节点组合。 所提出的EVEA算法优于所有基线，在四个现实世界的网络中，其超体积提高了高达19.3%，收敛速度提高了25%到40%，同时在影响、成本和时间目标之间保持了多样且平衡的帕累托前沿。