| 书目名称 | Link Prediction in Social Networks |
| 副标题 | Role of Power Law Di |
| 编辑 | Virinchi Srinivas,Pabitra Mitra |
| 视频video | |
| 概述 | accessible explanation of the role of power law degree distribution in link.Describes arange of link prediction algorithms in an easy-to-understand manner.Discusses the implementation of both the popu |
| 丛书名称 | SpringerBriefs in Computer Science |
| 图书封面 |  |
| 描述 | .Thiswork presents link prediction similarity measures for social networks that exploitthe degree distribution of the networks. In the context of link prediction indense networks, the text proposes similarity measures based on Markov inequalitydegree thresholding (MIDTs), which only consider nodes whose degree is above a thresholdfor a possible link. Also presented are similarity measures based on cliques(CNC, AAC, RAC), which assign extra weight between nodes sharing a greater numberof cliques. Additionally, a locally adaptive (LA) similarity measure isproposed that assigns different weights to common nodes based on the degreedistribution of the local neighborhood and the degree distribution of thenetwork. In the context of link prediction in dense networks, the textintroduces a novel two-phase framework that adds edges to the sparse graph toforma boost graph.. |
| 出版日期 | Book 2016 |
| 关键词 | Link Prediction; Power Law Degree Distribution; Local Neighborhood; Recommender Systems; Graph Mining |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-319-28922-9 |
| isbn_softcover | 978-3-319-28921-2 |
| isbn_ebook | 978-3-319-28922-9Series ISSN 2191-5768 Series E-ISSN 2191-5776 |
| issn_series | 2191-5768 |
| copyright | The Author(s) 2016 |
发表于 2025-3-21 17:46:41
