Citation graph

Its purpose is to acknowledge the relevance of the works of others to the topic of discussion at the point where the citation appears.

[3] References to single, machine-readable assertions in electronic scientific articles are known as nanopublications, a form of micro attributions.

In 1973, Henry Small published his work on co-citation analysis, which became a self-organizing classification system that led to document clustering experiments and eventually what is called "Research Reviews.

[6] Additionally, it has been found that the two methods – citation graph closeness and traditional content-based similarity – work well in conjunction to produce a more accurate result.

It has been found that analysing the citation graph for groups of documents in conjunction with keywords can provide an accurate way to identify clusters of similar research.

[7] In a similar vein, a way of identifying the main “stream” of an area of research, or the progression of a research idea over time can be identified by using depth first search algorithms on the citation graph.

While mostly effective, this method can lead to errors where a paper is recommended from a different discipline because of keyword matches even when the two topics actually have little in common.

In a similar way to the aforementioned search tools, constructions of citation graphs specific to the types of citations found in legal documents have been used to allow relevant past legal documents to be found when needed for a court decision.

The link weights between two authors in co-authorship networks can increase over time if they have further collaboration.

While citation graphs have had a noticeable impact on several areas of academia, they are likely to become more relevant in the future.

This makes the construction of these graphs very difficult, since it requires complex software analysis to extract citations from papers.