title: Sharp bounds and normalization of Wiener-type indices.
abstract: Complex networks abound in physical, biological and social sciences. Quantifying a network's topological structure facilitates network exploration and analysis, and network comparison, clustering and classification. A number of Wiener type indices have recently been incorporated as distance-based descriptors of complex networks, such as the R package QuACN. Wiener type indices are known to depend both on the network's number of nodes and topology. To apply these indices to measure similarity of networks of different numbers of nodes, normalization of these indices is needed to correct the effect of the number of nodes in a network. This paper aims to fill this gap. Moreover, we introduce an f-Wiener index of network G, denoted by Wf(G). This notion generalizes the Wiener index to a very wide class of Wiener type indices including all known Wiener type indices. We identify the maximum and minimum of Wf(G) over a set of networks with n nodes. We then introduce our normalized-version of f-Wiener index. The normalized f-Wiener indices were demonstrated, in a number of experiments, to improve significantly the hierarchical clustering over the non-normalized counterparts.
authors.affiliation: Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore.
authors.forename: Dechao
authors.initials: D
authors.lastname: Tian
authors.forename: Kwok Pui
authors.initials: KP
authors.lastname: Choi
mesh_terms: D008962:Models, Theoretical