The influence maximization problem in social networks aims to select a subset of most influential nodes, denoted as seed set, to maximize the influence diffusion of the seed nodes. The majority of existing works on this problem would ignite all the seed nodes simultaneously at the beginning of the diffusion process and let the influence diffuses passively in the network. However, it cannot depict the practical dynamics exactly of viral marketing campaigns in reality and fails to provide driving policies to control over the diffusion. In this paper, we focus on the dynamic influence maximization problem with limited budget to study the scheduling strategies including which influential node is to be seeded during the diffusion process and when to seed it at the right time. A time-dependent seed activating feedback scheme is modeled firstly by considering the time factor and its impact on the influence obligation in diffusion process. Then a scheduling heuristic based on determinate and latent margin is proposed to evaluate the marginal return of candidate nodes and activate the right seed node to promote the viral marketing. Extensive experiments on four social networks show that the proposed algorithm achieves significantly better results than a typical static influence maximization algorithm based on swarm intelligence and can improve the influence propagation under the time-dependent diffusion model comparing with the centrality-based scheduling heuristics.

中文翻译：

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有限预算影响最大化问题的基于确定和潜在边际的顺序种子调度启发式

社交网络中的影响力最大化问题旨在选择最有影响力的节点的子集，表示为种子集，以最大化种子节点的影响力扩散。针对这个问题的大多数现有工作会在扩散过程开始时同时点燃所有种子节点，让影响在网络中被动​​扩散。然而，它无法准确描述病毒式营销活动在现实中的实际动态，也无法提供控制传播的驱动策略。在本文中，我们专注于预算有限的动态影响最大化问题来研究调度策略，包括在扩散过程中播种哪些影响节点以及何时播种在正确的时间。首先考虑时间因素及其对扩散过程影响义务的影响，对时间相关的种子激活反馈方案进行建模。然后提出了一种基于确定和潜在边际的调度启发式算法来评估候选节点的边际回报并激活正确的种子节点以促进病毒式营销。在四个社交网络上的大量实验表明，与基于中心性的调度启发式算法相比，所提出的算法比典型的基于群体智能的静态影响最大化算法取得了明显更好的结果，并且可以改善时间相关扩散模型下的影响传播。然后提出了一种基于确定和潜在边际的调度启发式算法来评估候选节点的边际回报并激活正确的种子节点以促进病毒式营销。在四个社交网络上的大量实验表明，与基于中心性的调度启发式算法相比，所提出的算法比典型的基于群体智能的静态影响最大化算法取得了明显更好的结果，并且可以改善时间相关扩散模型下的影响传播。然后提出了一种基于确定和潜在边际的调度启发式算法来评估候选节点的边际回报并激活正确的种子节点以促进病毒式营销。在四个社交网络上的大量实验表明，与基于中心性的调度启发式算法相比，所提出的算法比典型的基于群体智能的静态影响最大化算法取得了明显更好的结果，并且可以改善时间相关扩散模型下的影响传播。