Suppawong Tuarob
ABSTRACT
Algorithms are an essential part of computational science. An algorithm search engine, which extracts pseudo-codes and their metadata from documents, and makes it searchable, has recently been developed as part of the CiteseerX suite. However, this algorithm search engine only retrieves and ranks relevant algorithms solely on textual similarity. Here, we propose a method for using the algorithm co-citation network to infer the similarity between algorithms. We apply a graph clustering algorithm on the network for algorithm recommendation and make suggestions on how to improve the current CiteseerX algorithm search engine.
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Digital Library
- S. Bhatia and P. Mitra. Summarizing Figures, Tables and Algorithms in Scientific Publications to Augment Search Results. ACM Transactions on Information Systems (TOIS), pages 1--24, 2010. Google ScholarDigital Library
- S. Bhatia, P. Mitra, and C. L. Giles. Finding algorithms in scientific articles. Proceedings of the 19th international conference on World wide web - WWW '10, page 1061, 2010. Google ScholarDigital Library
- S. Bhatia, S. Tuarob, P. Mitra, and C. L. Giles. An Algorithm Search Engine for Software Developers. SUITE '11: Proceedings of 2011 ICSE Workshop on Search-driven Development: Users, Infrastructure, Tools and Evaluation, 2011, 2011. Google ScholarDigital Library
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Index Terms
- Improving algorithm search using the algorithm co-citation network
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