Link prediction based on local major path degree
Link prediction based on local major path degree
复制标题
基于局部主路径度的链路预测
DOI：
10.1142/s0217984918503487
复制
发表时间：
2018
影响因子：
1.9
通讯作者：
Xiao Jie
中科院分区：
文献类型：
--
作者：
Yang Xu-Hua;Yang Xuhua;Ling Fei;Zhang Hai-Feng;Zhang Duan;Xiao Jie
链路预测可以根据网络节点、边和拓扑信息，估计复杂网络中任意两个不连通节点（两个种子节点）之间未知或未来边存在的概率。链接预测在社交网络、电子商务、数据挖掘、生物网络等领域具有重要的实用价值，正引起各领域科学家的广泛关注。在本文中，我们发现度分布和强度的两个和三个步骤的局部路径的两个种子节点之间可以揭示有效的相似性信息。提出了一种称为局部主路径度（LMPD）的指标来估计两个种子节点之间生成链接的概率。为了表明该算法的效率，我们比较了它与9个著名的相似性指数的基础上的局部信息在12个真实的网络。实验结果表明，LMPD算法具有较高的预测性能.
Link prediction can estimate the probablity of the existence of an unknown or future edges between two arbitrary disconnected nodes (two seed nodes) in complex networks on the basis of information regarding network nodes, edges and topology. With the important practical value in many fields such as social networks, electronic commerce, data mining and biological networks, link prediction is attracting considerable attention from scientists in various fields. In this paper, we find that degree distribution and strength of two- and three-step local paths between two seed nodes can reveal effective similarity information between the two nodes. An index called local major path degree (LMPD) is proposed to estimate the probability of generating a link between two seed nodes. To indicate the efficiency of this algorithm, we compare it with nine well-known similarity indices based on local information in 12 real networks. Results show that the LMPD algorithm can achieve high prediction performance.