Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory
Vrije Universiteit Amsterdam · Amsterdam UMC Location VUmc
Abstract
Graph theory is a valuable framework to study the organization of functional and anatomical connections in the brain. Its use for comparing network topologies, however, is not without difficulties. Graph measures may be influenced by the number of nodes (N) and the average degree (k) of the network. The explicit form of that influence depends on the type of network topology, which is usually unknown for experimental data. Direct comparisons of graph measures between empirical networks with different N and/or k can therefore yield spurious results. We list benefits and pitfalls of various approaches that intend to overcome these difficulties. We discuss the initial graph definition of unweighted graphs via…
Citation impact
- FWCI
- 19.15
- Percentile
- 100%
- References
- 86
Authors
3Topics & keywords
- Spurious relationship
- Clustering coefficient
- Random graph
- Network topology
- Exponential random graph models
- Computer science
- Graph theory
- Graph