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An Influence Maximization Algorithm Based on the Mixed Importance of Nodes

机译：基于节点混合重要性的影响最大化算法

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摘要

The influence maximization is the problem of finding k seed nodes that maximize the scope of influence in a social network. Therefore, the comprehensive influence of node needs to be considered, when we choose the most influential node set consisted of k seed nodes. On account of the traditional methods used to measure the influence of nodes, such as degree centrality, betweenness centrality and closeness centrality, consider only a single aspect of the influence of node, so the influence measured by traditional methods mentioned above of node is not accurate. In this paper, we obtain the following result through experimental analysis: the influence of a node is relevant not only to its degree and coreness, but also to the degree and coreness of the n-order neighbor nodes. Hence, we propose a algorithm based on the mixed importance of nodes to measure the comprehensive influence of node, and the algorithm we proposed is simple and efficient. In addition, the performance of the algorithm we proposed is better than that of traditional influence maximization algorithms.
机译：影响最大化是找到最大化社交网络影响范围的k种子节点的问题。因此，当我们选择最有影响力的节点组组成的节点组成时，需要考虑节点的全面影响。由于用于测量节点的影响的传统方法，例如程度中心，中心地位和接近中心，但仅考虑节点影响的单个方面，因此通过节点上面提到的传统方法测量的影响不准确。在本文中，我们通过实验分析获取以下结果：节点的影响不仅与其度和思考相关，而且对n阶邻居节点的程度和思忘。因此，我们提出了一种基于节点的混合重要性的算法来测量节点的全面影响，以及我们提出的算法简单富有高效。此外，我们提出的算法的性能优于传统影响最大化算法的算法。

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