𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Gaining scale-free and high clustering complex networks

✍ Scribed by Shouliang Bu; Bing-Hong Wang; Tao Zhou


Publisher
Elsevier Science
Year
2007
Tongue
English
Weight
330 KB
Volume
374
Category
Article
ISSN
0378-4371

No coin nor oath required. For personal study only.

✦ Synopsis


By making use of two observing facts for many natural and social networks, i.e., the nodes' diversity, and the disassortative (or assortative) properties for biological and technological (or social) networks, a simple and elegant model with three kinds of nodes and deterministic selective linking rule is proposed in this paper. We show that the given model can successfully capture two generic topological properties of many real networks: they are scale-free and they display a high degree of clustering. In practice, most models proposed to describe the topology of complex networks have difficulty to capture simultaneously these two features.


πŸ“œ SIMILAR VOLUMES


Parallelism in simulation and modeling o
✍ Tomas Hruz; Stefan Geisseler; Marcel SchΓΆngens πŸ“‚ Article πŸ“… 2010 πŸ› Elsevier Science 🌐 English βš– 745 KB

Evolution and structure of very large networks has attracted considerable attention in recent years. In this paper we study a possibility to simulate stochastic processes which move edges in a network leading to a scale-free structure. Scale-free networks are characterized by a ''fat-tail" degree di

Revisiting β€œscale-free” networks
✍ Evelyn Fox Keller πŸ“‚ Article πŸ“… 2005 πŸ› John Wiley and Sons 🌐 English βš– 242 KB

Recent observations of power-law distributions in the connectivity of complex networks came as a big surprise to researchers steeped in the tradition of random networks. Even more surprising was the discovery that power-law distributions also characterize many biological and social networks. Many at

Subgraph centrality and clustering in co
✍ Ernesto Estrada; Juan A. RodrΓ­guez-VelΓ‘zquez πŸ“‚ Article πŸ“… 2006 πŸ› Elsevier Science 🌐 English βš– 255 KB

The representation of complex systems as networks is inappropriate for the study of certain problems. We show several examples of social, biological, ecological and technological systems where the use of complex networks gives very limited information about the structure of the system. Consequently,