Link Prediction in Social Networks Role of Power Law Distribution / [electronic resource] :
by Virinchi Srinivas, Pabitra Mitra.
- 1st ed. 2016.
- IX, 67 p. 5 illus. in color. online resource.
- SpringerBriefs in Computer Science, 2191-5768 .
- SpringerBriefs in Computer Science, .
Introduction -- Link Prediction Using Degree Thresholding -- Locally Adaptive Link Prediction -- Two Phase Framework for Link Prediction -- Applications of Link Prediction -- Conclusion.
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.
9783319289229
10.1007/978-3-319-28922-9 doi
Computer science. Computer communication systems. Data mining. Computer Science. Data Mining and Knowledge Discovery. Computer Communication Networks.