Semantic Hubs and Authorities for Citation Analysis

We describe a method for analyzing the impact of papers on their field. We classify the paper’s citations by semantic meaning with numeric encoding and use them as weights on our extension of the Hubs and Authorities algorithm to determine the most authoritative papers. We compare our authorities to those determined by straight citation counts. While current common analysis methods only take into account the local citation counts, in part due to the difficulty of classifying large numbers of citations, we rely on graph analysis. We not only use the structure of the links among papers but also the semantics of the citations in order to get a step closer to the actual meanings underlying the citations. Introduction Tracking the impact or importance of a given paper, and by extension its author or authors, can be difficult . Existing methods using solely citation counts are considered by many to be inadequate (Adler, Ewing, & Taylor, 2008). For an example of the common pitfalls of straight citation counts, consider a controversial paper which draws a high volume of citations from papers that refute it; clearly high citation count here does not indicate established authority. The increasing tendency to base funding decisions on such metrics therefore is therefore troubling. An analysis method is needed that more closely reflects the actual impact of a paper. From a research perspective, it can also be difficult to discover which papers are the most important in a field of study, and to determine the relationships between the papers. It would be useful to have an overview saying which papers build on the work of, support, or criticize which other papers. Unfortunately this can currently only be done by reading all of the papers in question, which can be impractical due to the sheer number of papers in some fields. For example, at the ACM digital library the search term “citation analysis” retrieved 56,244 results. Current methods of analyzing the impact of a paper tend to look only at the citation count. While there are other metrics available, they generally focus on the author or journal rather than the paper itself. One of the most common methods currently used to analyze citations is the h-index (Hirsch, 2005), a metric developed by Jorge E. Hirsch in 2005 to quantify the contribution of a given researcher to their field. It is defined as the highest number x such that the researcher has published at least x articles, each of which has received at least x citations. While commonly used, it has a number of weaknesses. For example, it gives a very low score to researchers with a few, very highly cited papers; as Costas and Bordons (Costas & Bordons, 2007) said, “It is better to have 10 papers with 10 citations each than 5 papers with 200 citations each”. This and other similar metrics based purely on citation counts are insufficient because they don’t take citation types or the larger structure of the citation graph into account. More complicated methods such as CiteRank (Maslov & Redner, 2008), an adaptation of Google’s PageRank, still fail to take into account the semantic context of citations. P-Rank (Yan, Ding, & Sugimoto, 2011) takes into account the structure of the graph, including author, article, and journal, but not the semantic content of the citations. In this paper we look at the network of citations as a graph, with each paper being a node and each citation an edge. There are many algorithms for analyzing the structure of a graph; some