Algorithms are an essential part of computational science.
An algorithm search engine, which extracts pseudo-codes
and their metadata, and makes it searchable, has recently
been developed as part of the CiteseerX suite [3, 4]. However,
this algorithm search engine only retrieves and ranks
relevant algorithms based 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.
Simple patent citation count has been widely used for patent evaluation. We differentiate patent citations along two dimensions (assignees and technologies) into four types, and propose a weighted citation approach for assessing and ranking patents. We investigate five weight learning methods and compare their performance. Our weighted citation method performs consistently better than simple citation counts, in terms of rank correlations with patent renewal status. The estimated weights on different citations are consistent with economic insights on patent citations. Our study points to an interesting and promising research line on patent citation and network analysis that has not been explored.