Modeling of growing networks with directional attachment and communities.
In this paper, we propose a new network growth model and its learning algorithm to more precisely model such a real-world growing network as the Web. Unlike the conventional models, we have incorporated directional attachment and community structure for this purpose. We show that the proposed model exhibits a degree distribution with a power-law tail, which is an important characteristic of many large-scale real-world networks including the Web. Using real Web data, we experimentally show that predictive ability can be improved by incorporating directional attachment and community structure. Also, using synthetic data, we experimentally show that predictive ability can definitely be improved by incorporating community structure.
NTT Communication Science Laboratories, 2-4 Hikaridai, Seika-cho, Kyoto 619-0237, Japan. kimura@cslab.kecl.ntt.co.jp
Masahiro
M
Kimura
Kazumi
K
Saito
Naonori
N
Ueda
D000465:Algorithms; D016000:Cluster Analysis; D019058:Community Networks; D003198:Computer Simulation; D006801:Humans; D020407:Internet; D016571:Neural Networks, Computer