Add or change TechReport: MultiRank: Reputation Ranking for Generic Semantic Social Networks

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{{TWTR |id=TW-2009-09 }} {{Sbp.publication.inproceedings |type=Workshop Paper |bibtype=inproceedings |author=Xixi Luo, Joshua Shinavier |title=MultiRank: Reputation Ranking for Generic Semantic Social Networks |booktitle=1st International Workshop on Motivation and Incentives on the Web |address=Madrid, Spain |year=2009 |month=April |abstract=This paper presents a technique for calculating “reputation” or influence of users and artifacts in semantic social networks: in particular, as an incentive mechanism to encourage reuse of complex resources such as ontologies. Adapting the PageRank algorithm to the relational schemas of typical social network applications, this technique allows the programmer first to define via minimal rules the ways in which reputations of users and artifacts are likely to influence one another, then to obtain a mechanical, global ranking which reflects those rules in combination with the graph structure of the network. The mapping of multi-way relations such as usage and annotation to the binary-relational domain of PageRank is illustrated using the Actor-Concept-Instance model of ontologies. A lightweight software implementation, currently under development, will provide a convenient way to add reputation-based functionality to Java-based community applications. |pdf url=http://tw.rpi.edu/portal/Image:MultiRank.pdf }}

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