论文标题
超越信息交换：一种用于信息扩散的网络属性的方法
Beyond Information Exchange: An Approach to Deploy Network Properties for Information Diffusion
论文作者
论文摘要
在线社交网络中的信息扩散是社交网络分析领域的一个新的至关重要的问题，需要大量的研究关注。在各种情况下，有效的信息扩散至关重要。迄今为止，尽管已经开发了几种数学模型，但大流行预防，广告，营销等，但以前的作品缺乏系统的分析和探索邻里对信息扩散的影响。在本文中，我们提出了信息扩散算法的共同社区策略（CNS）算法，该算法证明了共同邻域在整个网络中信息传播中的作用。 CNS算法的性能在扩散速度和扩散方面在几个现实世界数据集上进行了评估，并与几种广泛使用的信息扩散模型相比。经验结果表明，中枢神经系统算法可以在扩散速度和扩散方面具有更好的信息扩散。
Information diffusion in Online Social Networks is a new and crucial problem in social network analysis field and requires significant research attention. Efficient diffusion of information are of critical importance in diverse situations such as; pandemic prevention, advertising, marketing etc. Although several mathematical models have been developed till date, but previous works lacked systematic analysis and exploration of the influence of neighborhood for information diffusion. In this paper, we have proposed Common Neighborhood Strategy (CNS) algorithm for information diffusion that demonstrates the role of common neighborhood in information propagation throughout the network. The performance of CNS algorithm is evaluated on several real-world datasets in terms of diffusion speed and diffusion outspread and compared with several widely used information diffusion models. Empirical results show CNS algorithm enables better information diffusion both in terms of diffusion speed and diffusion outspread.
