Link prediction in social networks : role of power law distribution

• 作者： Srinivas, Virinchi, author.
• 其他作者：
  • Mitra, Pabitra, author.
  • SpringerLink (Online service)
• 其他題名：
  • SpringerBriefs in computer science.
• 出版： Cham : Springer International Publishing :Imprint: Springer
• 叢書名： SpringerBriefs in computer science,
• 主題： Data mining. , Online social networks. , Computer Science. , Data Mining and Knowledge Discovery. , Computer Communication Networks.
• ISBN： 9783319289229 (electronic bk.) 、 9783319289212 (paper)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: Introduction -- Link Prediction Using Degree Thresholding -- Locally Adaptive Link Prediction -- Two Phase Framework for Link Prediction -- Applications of Link Prediction -- Conclusion.
• More
• 摘要註: This work presents link prediction similarity measures for social networks that exploit the degree distribution of the networks. In the context of link prediction in dense networks, the text proposes similarity measures based on Markov inequality degree thresholding (MIDTs), which only consider nodes whose degree is above a threshold for a possible link. Also presented are similarity measures based on cliques (CNC, AAC, RAC), which assign extra weight between nodes sharing a greater number of cliques. Additionally, a locally adaptive (LA) similarity measure is proposed that assigns different weights to common nodes based on the degree distribution of the local neighborhood and the degree distribution of the network. In the context of link prediction in dense networks, the text introduces a novel two-phase framework that adds edges to the sparse graph to forma boost graph.
• 讀者標籤：

  需登入
• 引用連結：

  Copy Link
• 目前引用數
• Share: