大葉大學圖書館 |

Link prediction in social networks[electronic resource] :role of power law distribution /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	006.312
書名/作者:	Link prediction in social networks : role of power law distribution // by Virinchi Srinivas, Pabitra Mitra.
作者:	Srinivas, Virinchi.
其他作者:	Mitra, Pabitra.
出版者:	Cham : : Springer International Publishing :, 2016.
面頁冊數:	ix, 67 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Data mining.
標題:	Online social networks.
標題:	Computer Science.
標題:	Data Mining and Knowledge Discovery.
標題:	Computer Communication Networks.
ISBN:	9783319289229
ISBN:	9783319289212
內容註:	Introduction -- Link Prediction Using Degree Thresholding -- Locally Adaptive Link Prediction -- Two Phase Framework for Link Prediction -- Applications of Link Prediction -- Conclusion.
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1007/978-3-319-28922-9

Link prediction in social networks[electronic resource] :role of power law distribution /
Srinivas, Virinchi.

Link prediction in social networksrole of power law distribution /[electronic resource] :by Virinchi Srinivas, Pabitra Mitra. - Cham :Springer International Publishing :2016. - ix, 67 p. :ill., digital ;24 cm. - SpringerBriefs in computer science,2191-5768. - SpringerBriefs in computer science..

Introduction -- Link Prediction Using Degree Thresholding -- Locally Adaptive Link Prediction -- Two Phase Framework for Link Prediction -- Applications of Link Prediction -- Conclusion.

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.

ISBN: 9783319289229

Standard No.: 10.1007/978-3-319-28922-9doiSubjects--Topical Terms:

337740
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Link prediction in social networks[electronic resource] :role of power law distribution /
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