What do we learn from correlations of local and global network properties?
arXiv:physics/0607150 · doi:10.1016/j.physa.2007.03.029
Abstract
In complex networks a common task is to identify the most important or "central" nodes. There are several definitions, often called centrality measures, which often lead to different results. Here we study extensively correlations between four local and global measures namely the degree, the shortest-path-betweenness, the random-walk betweenness and the subgraph centrality on different random-network models like Erdos-Renyi, Small-World and Barabasi-Albert as well as on different real networks like metabolic pathways, social collaborations and computer networks. Correlations are quite different between the real networks and the model networks questioning whether the models really reflect all important properties of the real world.
References in corpus (20)
- Emergence of scaling in random networks
- Statistical mechanics of complex networks
- The structure and function of complex networks
- Who is the best connected scientist? A study of scientific coauthorship networks
- Mean-field theory for scale-free random networks
- The scaling laws of human travel
- Attack vulnerability of complex networks
- Subgraph Centrality in Complex Networks
- Universal Behavior of Load Distribution in Scale-free Networks
- Weighted evolving networks: coupling topology and weights dynamics
- Growing networks with local rules: preferential attachment, clustering hierarchy and degree correlations
- Self-organization of collaboration networks
- Virtual Identification of Essential Proteins Within the Protein Interaction Network of Yeast
- Accurately modeling the Internet topology
- Spatial Growth of Real-world Networks
- Inferring Network Mechanisms: The Drosophila melanogaster Protein Interaction Network
- Network properties of written human language
- Scale-Free Networks Generated By Random Walkers
- Self Organized Scale-Free Networks from Merging and Regeneration
- Nonlocal evolution of weighted scale-free networks