EAN: 9783319289212

SPRINGERBRIEFS IN COMPUTER SCIENCE LINK PREDI...

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.

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