Link prediction method based on matching degree of resource transmission for complex network

被引：0

作者：

Liu S. ^{[1
,2
]}

Li X. ^{[1
,2
]}

Chen H. ^{[1
,2
]}

Wang K. ^{[1
,2
]}

机构：

[1] Information Technology Institute, Information Engineering University, Zhengzhou

[2] National Digital Switching System & Engineering Technology Research Center, Zhengzhou

来源：

| 1600年 / Editorial Board of Journal on Communications卷 / 41期

基金：

中国国家自然科学基金;

关键词：

Complex network; Link prediction; Matching degree; Resource transmission;

D O I：

10.11959/j.issn.1000-436x.2020124

中图分类号：

学科分类号：

摘要：

In order to solve the problem that many existing resource-transmission-based methods ignore the important influence of the matching degree of two endpoints on resource transmission, a link prediction method was proposed based on matching degree of resource transmission for complex networks. Firstly, by analyzing the two endpoints on the resource transmission path in detail, the method of quantifying the matching degree between two nodes was proposed. Then, in order to describe the influence of matching degree on resource transmission process between nodes, the matching degree of resource transmission was defined. Finally, based on the matching degree of resource transmission, a resource transmission matching index was proposed considering the resource amount of bidirectional transmission between nodes. The experimental results of nine datasets show that compared with other similarity indices, the proposed index can achieve higher prediction accuracy under the AUC and Precision metrics. © 2020, Editorial Board of Journal on Communications. All right reserved.

引用

页码：70 / 79

页数：9

共 32 条

[21]

MELAMED D, HARRELL A, SIMPSON B., Cooperation, clustering, and assortative mixing in dynamic networks, Proceedings of the National Academy of Sciences, 115, 5, pp. 951-956, (2018)

[22]

WU Y, YU H, ZHANG J, Et al., USI-AUC: an evaluation criterion of community detection based on a novel link-prediction method, Intelligent Data Analysis, 22, 2, pp. 439-462, (2018)

[23]

CHUAN P M, ALI M, KHANG T D, Et al., Link prediction in co-authorship networks based on hybrid content similarity metric, Applied Intelligence, 48, 8, pp. 2470-2486, (2018)

[24]

GOPAL S., The evolving social geography of blogs, pp. 275-293, (2007)

[25]

ULANOWICZ R E, DEANGELIS D L., Network analysis of trophic dynamics in South Florida ecosystems, US Geological Survey Program on the South Florida Ecosystem, 114, pp. 45-47, (2005)

[26]

WATTS D J, STROGATZ S H., Collective dynamics of 'small-world' Networks, Nature, 393, 6684, (1998)

[27]

GUIMERA R, DANON L, DIAZ-GUILERA A, Et al., Self-similar Community structure in a network of human interactions, Physical Review E, 68, 6, (2003)

[28]

ADAMIC L A, GLANCE N., The political blogosphere and the 2004 US election: divided they blog, Proceedings of the 3rd International Workshop on Link Discovery, pp. 36-43, (2005)

[29]

BATAGELJ V, MRVAR A., Pajek-program for large network analysis, Connections, 21, 2, pp. 47-57, (1998)

[30]

LYU L Y, PAN L M, ZHOU T, Et al., Toward link predictability of complex networks, Proceedings of the National Academy of Sciences, 112, 8, pp. 2325-2330, (2015)

← 1 2 3 4 →