Benchmarking link analysis ranking methods in assistive environments

Several assistive applications exhibit a network structure. Characterizing the structure of such networks is critical in many assistive applications. Existing methods in the of social network analysis aim to detect, analyze, and summarize interesting or surprising components and trends in the network. In this paper, we provide a benchmark of two graph ranking methods: pagerank and HITS. The methods are tested on real social network data from three different domains: citation graphs, road networks, and a subgraph of Google. Our findings suggest that the quality of the ranking as well as the speed of convergence of both algorithms highly depends on the underlying network structure