By Srinivas Virinchi,Pabitra Mitra
work provides 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 according to Markov inequality
degree thresholding (MIDTs), which in simple terms examine nodes whose measure is above a threshold
for a potential hyperlink. additionally offered are similarity measures in keeping with 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 varied weights to universal nodes in accordance 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 singular two-phase framework that provides edges to the sparse graph to
forma develop graph.
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Additional info for Link Prediction in Social Networks: Role of Power Law Distribution (SpringerBriefs in Computer Science)
Link Prediction in Social Networks: Role of Power Law Distribution (SpringerBriefs in Computer Science) by Srinivas Virinchi,Pabitra Mitra