Link Prediction in Social Networks
Role of Power Law Distribution
Nonfiction, Computers, Networking & Communications, Hardware, Database Management, General Computing
| Author: | Pabitra Mitra, Srinivas Virinchi | ISBN: | 9783319289229 |
| Publisher: | Springer International Publishing | Publication: | January 22, 2016 |
| Imprint: | Springer | Language: | English |
| Author: | Pabitra Mitra, Srinivas Virinchi |
| ISBN: | 9783319289229 |
| Publisher: | Springer International Publishing |
| Publication: | January 22, 2016 |
| Imprint: | Springer |
| Language: | 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.
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.
