The study of some structural properties of networks is introduced from a graph spectral perspective. First, subgraph centrality of nodes is defined and used to classify essential proteins in a proteomic map. This index is then used to produce a method that allows the identification of superhomogeneous networks. At the same time this method classify non-homogeneous network into three universal classes of structure. We give examples of these classes from networks in different real-world scenarios. Finally, a communicability function is studied and showed as an alternative for defining communities in complex networks. Using this approach a community is unambiguously defined and an algorithm for its identification is proposed and exemplified in a real-world network.
|Title of host publication||Structural, Syntactic, and Statistical Pattern Recognition|
|Number of pages||17|
|Publication status||Published - 28 Aug 2010|
|Name||Lecture Notes in Computer Science|
- subgraph centrality
- Estrada index
- network communities