摘要
为解决LDAG(DAG Algorithm Based on Linear Threshold)算法在处理关于社会网络影响力最大化过程中,优先考虑网络影响力传播模型、忽视社会网络的拓扑结构问题,利用社交网络社区的结构,有针对性地选择影响力传播的关键节点,对LDAG算法进行了改进。利用关键节点简化了有向无环图的构造过程,保证了其高精度与运行效率高的特点,同时也优化了算法的时间复杂度和空间复杂度。通过两个有效的实验数据集对算法进行验证,结果表明改进的算法可以大幅度降低算法的运行时间,且对算法的精度影响很小。
LDAG( DAG algorithm based on linear threshold) algorithm is a heuristic algorithm for maximizing the influence of social networks. It has the characteristics of high accuracy and high efficiency. When solving the problem of maximizing the influence of social networks,the network influence propagation model is given priority,and then the topological structure of social networks is ignored. In this paper,the LDAG algorithm is improved by using the structure of social network community to select the key nodes of influence propagation.The key nodes are used to simplify the construction process of directed acyclic graph,optimize the time complexity and space complexity of the algorithm,and validate the rationality of the algorithm by using two effective experimental data sets.
引文
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