By Srinivas Virinchi,Pabitra Mitra
This
work offers hyperlink prediction similarity measures for social networks that exploit
the measure distribution of the networks. within the context of hyperlink prediction in
dense networks, the textual content proposes similarity measures in accordance with Markov inequality
degree thresholding (MIDTs), which in basic terms examine nodes whose measure is above a threshold
for a potential hyperlink. additionally offered are similarity measures in response to cliques
(CNC, AAC, RAC), which assign excess weight among nodes sharing a better number
of cliques. also, a in the neighborhood adaptive (LA) similarity degree is
proposed that assigns assorted weights to universal nodes in keeping with the degree
distribution of the neighborhood local and the measure distribution of the
network. within the context of hyperlink prediction in dense networks, the text
introduces a unique two-phase framework that provides edges to the sparse graph to
forma strengthen graph.
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Extra info for Link Prediction in Social Networks: Role of Power Law Distribution (SpringerBriefs in Computer Science)
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