首页> 外文会议>International Conference on Frontiers of Intelligent Computing : Theory and Applications >Rank Consensus Between Importance Measures in Hypergraph Model of Social Network
【24h】

Rank Consensus Between Importance Measures in Hypergraph Model of Social Network

机译:社交网络超图模型中的重要性措施之间的共识

获取原文

摘要

In social network (SN), a node is considered as a social entity and a link defines the connection between social entities. In general, the link is shown as a dyadic relationship which is unable to represent a group having super-dyadic relationship. Hypergraph model of a network preserves the super-dyadic relation between the nodes. Several algorithms have been developed to measure the node importance and ranking the nodes according to importance. Some measures take less time, whereas some take more time. We propose a method to find the correlation between the different importance measures in hypergraph. By establishing high correlation, the ranking of a time inefficient importance measure can be computed from a time-efficient measure. In this paper, we present our contribution in twofold. At first, we show the construction of primal/Gaifman graph from hypergraph. Secondly, we establish the correlation between the different importance measures that are used for ranking the nodes of a hypergraph.
机译:在社交网络(SN)中,节点被视为社会实体,链接定义社会实体之间的连接。通常,链接显示为不能代表具有超级二级关系的组的二元关系。网络的超图模型保留节点之间的超级二级关系。已经开发了几种算法来测量节点重要性,并根据重要性排列节点。有些措施需要更少的时间,而有些人需要更多的时间。我们提出了一种方法来找到超图中不同重要措施之间的相关性的相关性。通过建立高相关,可以从节省时间测量计算时间效率低下的排名。在本文中,我们展示了我们在双重的贡献。首先,我们展示了超图的原始/ Gaifman图的构建。其次,我们建立了用于排名超图的节点的不同重要性测量之间的相关性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号