论文标题
EMH：基于邻里多样性的社交网络中重要用户的扩展混合H-指数中心性
EMH: Extended Mixing H-index centrality for identification important users in social networks based on neighborhood diversity
论文作者
论文摘要
社交网络的快速扩展为用户提供了一个合适的平台。通过社交网络，我们可以在很短的时间内收集资源并共享消息。社交网络的发展为我们带来了巨大的便利。但是，组成网络的节点具有不同的扩展能力，这些传播能力受许多因素限制，而网络的拓扑结构是主要元素。为了更准确地计算节点在网络中的重要性，本文根据相邻节点的多样性来定义改进的H-指数中心性（IH），然后使用累积中心（MC）考虑所有相邻的节点，并提出了扩展的混合H-Index Centration（EMH）。我们通过易感感染的（SIR）模型和单调性评估所提出的方法，该方法分别用于评估该方法的准确性和分辨率。实验结果表明，所提出的方法优于识别不同网络中节点的现有度量。
The rapid expansion of social network provides a suitable platform for users to deliver messages. Through the social network, we can harvest resources and share messages in a very short time. The developing of social network has brought us tremendous conveniences. However, nodes that make up the network have different spreading capability, which are constrained by many factors, and the topological structure of network is the principal element. In order to calculate the importance of nodes in network more accurately, this paper defines the improved H-index centrality (IH) according to the diversity of neighboring nodes, then uses the cumulative centrality (MC) to take all neighboring nodes into consideration, and proposes the extended mixing H-index centrality (EMH). We evaluate the proposed method by Susceptible-Infected-Recovered (SIR) model and monotonicity which are used to assess accuracy and resolution of the method, respectively. Experimental results indicate that the proposed method is superior to the existing measures of identifying nodes in different networks.
