The influence maximization problem (IMP) is one of the most important topics in social network analysis. It consists of finding the smallest seed of users that maximizes the influence spread in a social network. The main influence spread models are the linear threshold model (LT-model) and the independent cascade model (IC-model). These models have mainly been treated by using the single-objective paradigm which covers just one perspective: maximize the influence spread starting by given seed size, or minimize the seed set to reach a given number of influenced nodes. Sometimes, this minimization problem has been called the least cost influence problem (LCI). In this work, we propose a new optimization model for both perspectives under conflict, through the LT-model, by applying a binary multi-objective approach. Swarm intelligence methods are implemented to solve our proposal on real networks. Results are promising and suggest that the new multi-objective solution proposed can be properly solved in harder instances.

影响力最大化问题（IMP）是社交网络分析中最重要的主题之一。它包括找到使用户在社交网络中传播的影响最大化的最小种子。主要影响扩散模型是线性阈值模型（LT模型）和独立级联模型（IC模型）。这些模型主要通过使用仅涵盖一个角度的单目标范例进行处理：从给定种子大小开始最大化影响散布，或最小化种子集以达到给定数量的受影响节点。有时，这种最小化问题被称为最小成本影响问题（LCI）。在这项工作中，我们通过应用二元多目标方法，通过LT模型，针对冲突下的两个视角提出了一个新的优化模型。实现了群体智能方法以解决我们在真实网络上的建议。结果是有希望的，并提出可以在较困难的情况下正确解决提出的新的多目标解决方案。