Clustering under the line graph transformation: Application to reaction network

Abstract

Many real networks can be understood as two complementary networks with two kind of nodes. This is the case of metabolic networks where the first network has chemical compounds as nodes and the second one has nodes as reactions. The second network can be related to the first one by a technique called line graph transformation (i.e., edges in an initial network are transformed into nodes). Recently, the main topological properties of the metabolic networks have been properly described by means of a hierarchical model. In our work, we apply the line graph transformation to a hierarchical network and the clustering coefficient $C(k)$ is calculated for the transformed network, where $k$ is the node degree. While $C(k)$ follows the scaling law $C(k)\sim k^{-1.1}$ for the initial hierarchical network, $C(k)$ scales weakly as $k^{0.08}$ for the transformed network. These results indicate that the reaction network can be identified as a degree-independent clustering network.

20 pages, 12 figures, REVTeX 4 style

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Cited by in corpus (5)

• Line Graphs of Weighted Networks for Overlapping Communities
• Line graphs as social networks
• Flexible construction of hierarchical scale-free networks with general exponent
• Assortativity in random line graphs
• Toward Edge-Centric Network Embeddings