Abstract
SimRank is one measure that compute the similarities between nodes in applications, where the returning of top-k query lists is often required. In this paper, we adopt SimRank as the similarity computation measure and re-write the original inefficient iterative equation into a non-iterative one, we call it Eigen-SimRank. We focus on multi-relational networks, where there may exist different kinds of relationships among nodes and query results may change with different perspectives. In order to compute a top-k query list under any perspective especially compound perspective, we suggest dynamic updating algorithm and rank aggregation methods. We evaluate our algorithms in the experiment section.
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Xu, J., Li, C., Chen, H., Sun, H. (2015). SimRank Based Top-k Query Aggregation for Multi-Relational Networks. In: Dong, X., Yu, X., Li, J., Sun, Y. (eds) Web-Age Information Management. WAIM 2015. Lecture Notes in Computer Science(), vol 9098. Springer, Cham. https://doi.org/10.1007/978-3-319-21042-1_59
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DOI: https://doi.org/10.1007/978-3-319-21042-1_59
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