This paper aims to investigate information-theoretic network complexity measures which have already been intensely used in mathematical- and medicinal chemistry including drug design.Numerous such measures have been developed so far but many 3282779348850 of them lack a meaningful interpretation, e.g.
, we want to examine which kind of structural information they detect.Therefore, our main contribution is to shed light on the relatedness between some selected information measures for graphs by performing a large scale analysis using chemical networks.Starting from several sets mqr93ll/a containing real and synthetic chemical structures represented by graphs, we study the relatedness between a classical (partition-based) complexity measure called the topological information content of a graph and some others inferred by a different paradigm leading to partition-independent measures.
Moreover, we evaluate the uniqueness of network complexity measures numerically.Generally, a high uniqueness is an important and desirable property when designing novel topological descriptors having the potential to be applied to large chemical databases.