By Srinivas Virinchi,Pabitra Mitra
ISBN-10: 3319289217
ISBN-13: 9783319289212
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 simply reflect on nodes whose measure is above a threshold
for a potential hyperlink. additionally awarded are similarity measures in accordance with cliques
(CNC, AAC, RAC), which assign excess weight among nodes sharing a better number
of cliques. also, a in the community adaptive (LA) similarity degree is
proposed that assigns diversified 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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Extra info for Link Prediction in Social Networks: Role of Power Law Distribution (SpringerBriefs in Computer Science)
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Link Prediction in Social Networks: Role of Power Law Distribution (SpringerBriefs in Computer Science) by Srinivas Virinchi,Pabitra Mitra
by William
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