Share
Link Prediction in Social Networks: Role of Power law Distribution (Springerbriefs in Computer Science) (in English)
Virinchi Srinivas; Pabitra Mitra (Author)
·
Springer
· Paperback
Link Prediction in Social Networks: Role of Power law Distribution (Springerbriefs in Computer Science) (in English) - Virinchi Srinivas; Pabitra Mitra
$ 52.09
$ 54.99
You save: $ 2.90
Choose the list to add your product or create one New List
✓ Product added successfully to the Wishlist.
Go to My WishlistsIt will be shipped from our warehouse between
Thursday, June 06 and
Friday, June 07.
You will receive it anywhere in United States between 1 and 3 business days after shipment.
Synopsis "Link Prediction in Social Networks: Role of Power law Distribution (Springerbriefs in Computer Science) (in English)"
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.
- 0% (0)
- 0% (0)
- 0% (0)
- 0% (0)
- 0% (0)
All books in our catalog are Original.
The book is written in English.
The binding of this edition is Paperback.
✓ Producto agregado correctamente al carro, Ir a Pagar.