Link Prediction in Co-Authorship Network Under Fuzziness and Application in Biomedical Analysis

被引：0

作者：

Das, Kousik ^{[1
]}

Maity, Ananta ^{[2
]}

De, Kajal ^{[1
,3
]}

Mondal, Sukumar ^{[2
]}

Samanta, Sovan ^{[4
,5
]}

Allahviranloo, Tofigh ^{[5
]}

机构：

[1] Netaji Subhas Open Univ, Dept Math, Kolkata 700091, India

[2] Raja Narendra Lal Khan Womens Coll, Dept Math, Midnapore 721102, India

[3] Diamond Harbour Womens Univ, Kolkata 743368, India

[4] Western Caspian Univ, Dept Tech Sci, Baku 1001, Azerbaijan

[5] Istinye Univ, Res Ctr Performance & Prod Anal, TR-34460 Istanbul, Turkiye

来源：

FUZZY INFORMATION AND ENGINEERING | 2024年 / 16卷 / 02期

关键词：

social network; path; link prediction; COMPLEX NETWORKS; NEIGHBORS;

D O I：

10.26599/FIE.2024.9270039

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

We aim to predict links in fuzzy social networks, where the existing methods based on common neighbors of two nodes are not effective. These methods are local measures that only work when the shortest distance between two nodes is less than or equal to two. Our method can handle cases where the shortest distance is between three and five. We define the concepts of link strength and path strength in a network and propose an algorithm for predicting links. We illustrate our method with a numerical example in a co-authorship network and discuss application areas in biomedical.

引用

页码：155 / 174

页数：20

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