Generating multi-scaling networks with different types of nodes
arXiv:cond-mat/0508338 · doi:10.1016/j.physa.2006.02.048
Abstract
A variety of scale-free networks have been created since the pioneer work by A.-L. Barabási and R. Albert. All this networks are homogeneous since they are composed of the same kind of nodes. In the realistic world, however, one element (node or vertex) in the network may play different roles and hence has different functions. In this Letter, we develop a new kind of network to account for this property. In our model, each type of nodes may exhibit a scaling law in the degree distribution and the scaling exponents are adjustable. As a consequence, the whole network lacks of such scaling characteristics, which indicates that many previous statistical results based on empirical data that claimed to be scale-free networks may need to be reexamined. This model poses an alternative way of the network division other than the module method. Besides, one can expect that this new network will exhibit some interesting properties concerning the dynamical processes on it.
4 figures
References in corpus (19)
- Emergence of scaling in random networks
- Statistical mechanics of complex networks
- Finding and evaluating community structure in networks
- Finding community structure in very large networks
- Epidemic spreading in scale-free networks
- Uncovering the overlapping community structure of complex networks in nature and society
- The diameter of the world wide web
- Hierarchical organization of modularity in metabolic networks
- Evolution of networks
- Hierarchical Organization in Complex Networks
- Clustering and preferential attachment in growing networks
- Dynamical and correlation properties of the Internet
- Why social networks are different from other types of networks
- Measuring preferential attachment for evolving networks
- Class of correlated random networks with hidden variables
- Fitness-dependent topological properties of the World Trade Web
- The Fractal Properties of Internet
- Exploring complex networks by walking on them
- Small-World Networks: Links with long-tailed distributions