Link Prediction in Social Networks
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- Synopsis
- 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.
- Copyright:
- 2016
Book Details
- Book Quality:
- Publisher Quality
- ISBN-13:
- 9783319289229
- Publisher:
- Springer International Publishing, Cham
- Date of Addition:
- 09/15/16
- Copyrighted By:
- Springer
- Adult content:
- No
- Language:
- English
- Has Image Descriptions:
- No
- Categories:
- Nonfiction, Social Studies
- Submitted By:
- Bookshare Staff
- Usage Restrictions:
- This is a copyrighted book.