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A Method of Social Network Node Preference Evaluation Based on the Topology Potential

机译:基于拓扑潜力的社交网络节点优先评估方法

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This paper reports a hypergraph model for online social networks with an emphasis on the node preference. Some improvements of the model are made in the present study. First, the inherent nodes properties and their links are utilized in the proposed evaluation model. Second, the proposed model contains a topology potential value of node, which is based on cognitive data field in physics. In the calculation of node quality entropy - weight method are used. In way, human interference factors can be obtained for estimating node quality. The calculation of shortest path is based on the Dijkstra hypergraph. Third, a replacing algorithm is employed to account for node preference by modifying deleting algorithm. Then, based on the node preference and the feature that nodes are attracted each other in data field to form a community, we propose a hyper-graph model for the function of social networks community detection. The model is experimented to prove the validity and usability of evaluation results.
机译:本文报告了在线社交网络的超图模型,重点是节点偏好。在本研究中制定了该模型的一些改进。首先,在所提出的评估模型中使用固有节点属性及其链接。其次,所提出的模型包含节点的拓扑潜在值,它基于物理学中的认知数据字段。在计算节点质量熵的计算中使用。以便,可以获得人机干扰因子以估计节点质量。最短路径的计算基于Dijkstra超图。第三,采用更换算法来通过修改删除算法来解释节点首选项。然后,基于节点首选项和节点在数据字段中吸引的特征来形成社区,我们提出了一种用于社交网络群落检测功能的超图模型。该模型进行了实验,以证明评估结果的有效性和可用性。

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