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Link Prediction in Social Networks: Role of Power Law Distribution (SpringerBriefs in Computer Science) - Softcover
Synopsis
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.
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About the Author
Dr. Virinchi Srinivas is a Graduate Research Assistant in the Department of Computer Science at the University of Maryland, College Park, MD, USA.
Dr. Pabitra Mitra is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology, Kharagpur, India.
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Link Prediction in Social Networks
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 80 pp. Englisch. Seller Inventory # 9783319289212
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Link Prediction in Social Networks : Role of Power Law Distribution
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Seller Inventory # 9783319289212
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Link Prediction in Social Networks
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents anaccessible explanation of the role of power law degree distribution in linkpredictionDescribes arange of link prediction algorithms in an easy-to-understand mannerDiscusses the implementation of both the popularlink prediction algor. Seller Inventory # 106033966
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