徐行, 陈红梅, 周丽华. 异质社会网中基于信息熵的影响最大化算法[J]. 云南大学学报(自然科学版), 2022, 44(4): 698-707. doi: 10.7540/j.ynu.20210617
引用本文: 徐行, 陈红梅, 周丽华. 异质社会网中基于信息熵的影响最大化算法[J]. 云南大学学报(自然科学版), 2022, 44(4): 698-707. doi: 10.7540/j.ynu.20210617
XU Hang, CHEN Hong-mei, ZHOU Li-hua. Influence maximization algorithm based on information entropy in heterogeneous social networks[J]. Journal of Yunnan University: Natural Sciences Edition, 2022, 44(4): 698-707. DOI: 10.7540/j.ynu.20210617
Citation: XU Hang, CHEN Hong-mei, ZHOU Li-hua. Influence maximization algorithm based on information entropy in heterogeneous social networks[J]. Journal of Yunnan University: Natural Sciences Edition, 2022, 44(4): 698-707. DOI: 10.7540/j.ynu.20210617

异质社会网中基于信息熵的影响最大化算法

Influence maximization algorithm based on information entropy in heterogeneous social networks

  • 摘要: 提出了异质社会网中基于信息熵的影响最大化算法(Influence Maximization algorithm based on Information Entropy in Heterogeneous social networks,IMIEH). 首先,考虑不同类型节点所携带信息不同以及不同节点所传递信息不同,提出参与熵和交互熵的概念,进而计算节点间的影响权重;然后,基于线性阈值模型,计算节点的全局影响;最后,根据节点的边际增益选择种子集. 实验结果表明,与MPIE,SimPath和DAGIM算法相比,提出算法选择的种子集具有更大的影响范围.

     

    Abstract: The Influence Maximization algorithm based on Information Entropy in Heterogeneous social networks (IMIEH) is proposed in this paper. Firstly, the participation entropy and interaction entropy of nodes are proposed due to the different information carried and transmitted by different types of nodes, further the influence weight between nodes is presented. Then, based on the linear threshold model, the global influence of nodes is defined. Finally, the seed set is computed according to the marginal gain of nodes. Experimental results show that compared with MPIE, SimPath and DAGIM algorithms, the seed set obtained by the proposed algorithm IMIEH has a larger influence range.

     

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