Matrix computations (3rd ed.)
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
CNSR '04 Proceedings of the Second Annual Conference on Communication Networks and Services Research
SemRank: ranking complex relationship search results on the semantic web
WWW '05 Proceedings of the 14th international conference on World Wide Web
Object-level ranking: bringing order to Web objects
WWW '05 Proceedings of the 14th international conference on World Wide Web
Exploiting the hierarchical structure for link analysis
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Objectrank: authority-based keyword search in databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Finding and ranking knowledge on the semantic web
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
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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.