  We consider the problem of rank aggregation based on new distance measures
derived through axiomatic approaches and based on score-based methods. In the
first scenario, we derive novel distance measures that allow for discriminating
between the ranking process of highest and lowest ranked elements in the list.
These distance functions represent weighted versions of Kendall's tau measure
and may be computed efficiently in polynomial time. Furthermore, we describe
how such axiomatic approaches may be extended to the study of score-based
aggregation and present the first analysis of distributed vote aggregation over
networks.
