Pregled bibliografske jedinice broj: 720664
Community structure in networks: Girvan-Newman algorithm improvement
Community structure in networks: Girvan-Newman algorithm improvement // Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2014
Opatija, Hrvatska: Institute of Electrical and Electronics Engineers (IEEE), 2014. str. 997-1002 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 720664 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Community structure in networks: Girvan-Newman
algorithm improvement
Autori
Despalatović, Ljiljana ; Vojković, Tanja ; Vukičević, Damir
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2014
/ - : Institute of Electrical and Electronics Engineers (IEEE), 2014, 997-1002
ISBN
978-953-233-081-6
Skup
Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2014 37th International Convention on
Mjesto i datum
Opatija, Hrvatska, 26.05.2014. - 30.05.2014
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
complex networks computational complexity graph theory iterative methods
Sažetak
Real world networks often have community structure. It is characteristic that the groups of nodes are connected denser within themselves and rarely with each other. The Girvan-Newman method for the detection and analysis of community structure is based on the iterative elimination of edges with the highest number of the shortest paths that go through them. By eliminating edges the network breaks down into smaller networks, i.e. communities. This paper introduces improved Girvan-Newman method where multi-edge removal is allowed, and presents the results of the application of both methods to the existing real social network (Zachary karate club), the computergenerated network and the tumor genes and their mutations network. The improved algorithm in practice reduces the number of operations, but retains the same computational complexity, so it cannot be applied to networks with a very large number of nodes. The most important feature of our improvement is that the result is graph- theoretical invariant, while original algorithm depends on the vertex labeling.
Izvorni jezik
Engleski
Znanstvena područja
Matematika, Računarstvo, Informacijske i komunikacijske znanosti
POVEZANOST RADA
Ustanove:
Prirodoslovno-matematički fakultet, Split,
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