On graph entropy measures for knowledge discovery from publication network data

Abstract

Many research problems are extremely complex, making in- terdisciplinary knowledge a necessity; consequently cooperative work in mixed teams is a common and increasing research procedure. In this pa- per, we evaluated information-theoretic network measures on publication networks. For the experiments described in this paper we used the net- work of excellence from the RWTH Aachen University, described in [1]. Those measures can be understood as graph complexity measures, which evaluate the structural complexity based on the corresponding concept. We see that it is challenging to generalize such results towards different measures as every measure captures structural information differently and, hence, leads to a different entropy value. This calls for exploring the structural interpretation of a graph measure [2] which has been a challenging problem.