Measuring Proximity in Attributed Networks for Community Detection

被引：1

作者：

Aynulin, Rinat ^{[1
,3
]}

Chebotarev, Pavel ^{[2
]}

机构：

[1] Moscow Inst Phys & Technol, 9 Inst Skii Per, Dolgoprudnyi 141700, Moscow Region, Russia

[2] Russian Acad Sci, Trapeznikov Inst Control Sci, 65 Profsoyuznaya Str, Moscow 117997, Russia

[3] Russian Acad Sci, Kotelnikov Inst Radioengn & Elect IRE, Mokhovaya 11-7, Moscow 125009, Russia

来源：

COMPLEX NETWORKS & THEIR APPLICATIONS IX, VOL 1, COMPLEX NETWORKS 2020 | 2021年 / 943卷

关键词：

Attributed networks; Community detection; Proximity measure; Kernel on graph; COLLABORATIVE RECOMMENDATION; DISTANCES; CRITERIA; KERNELS;

D O I：

10.1007/978-3-030-65347-7_3

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Proximity measures on graphs have a variety of applications in network analysis, including community detection. Previously they have been mainly studied in the context of networks without attributes. If node attributes are taken into account, however, this can provide more insight into the network structure. In this paper, we extend the definition of some well-studied proximity measures to attributed networks. To account for attributes, several attribute similarity measures are used. Finally, the obtained proximity measures are applied to detect the community structure in some real-world networks using the spectral clustering algorithm.

引用

页码：27 / 37

页数：11

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