K-means-based algorithm for blockmodeling linked networks

Povzetek

The paper presents a k-means-based algorithm for blockmodeling linked networks where linked networks are defined as a collection of one-mode and two-mode networks in which units from different one-mode networks are connected through two-mode networks. The reason for this is that a faster algorithm is needed for blockmodeling linked networks that can better scale to larger networks. Examples of linked networks include multilevel networks, dynamic networks, dynamic multilevel networks, and meta-networks. Generalized blockmodeling has been developed for linked/multilevel networks, yet the generalized blockmodeling approach is too slow for analyzing larger networks. Therefore, the flexibility of generalized blockmodeling is sacrificed for the speed of k-means-based approaches, thus allowing the analysis of larger networks. The presented algorithm is based on the two-mode k-means (or KL-means) algorithm for two-mode networks or matrices. As a side product, an algorithm for one-mode blockmodeling of one-mode networks is presented. The algorithm's use on a dynamic multilevel network with more than 400 units is presented. A situation study is also conducted which shows that k-means based algorithms are superior to relocation algorithm-based methods for larger networks (e.g. larger than 800 units) and never much worse.

Ključne besede

Analiza omrežij;Bločno modeliranje;Algoritmi;

Podatki

Jezik:	Angleški jezik
Leto izida:	2020
Tipologija:	1.01 - Izvirni znanstveni članek
Organizacija:	UL FDV - Fakulteta za družbene vede
UDK:	303
COBISS:	36567901
ISSN:	0378-8733
Št. ogledov:	369
Št. prenosov:	92
Ocena:	0 (0 glasov)
Metapodatki:

Ostali podatki

Sekundarne ključne besede:	Network analysis;Blockmodeling;Algorithms;
Vrsta dela (COBISS):	Članek v reviji
Strani:	str. 153-169
Zvezek:	ǂVol. ǂ61
Čas izdaje:	May 2020
DOI:	10.1016/j.socnet.2019.10.006
ID:	13130076