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445:, the impact a particular paper has had on the academic world. While a hard value to quantify, scholarly impact is useful, as having a measure of scholarly impact for many papers can aid in identifying important papers. It can also provide a measure of the relevance of a particular academic community. Citation graphs are very useful in measuring this as the number of connections on the citation graph corresponds with the scholarly impact of an article, as this means it has been cited by many other papers.
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366:; that is, there are no loops in the graph. This is not always the case in practice, since an academic paper goes through several versions in the publishing process. The timing of asynchronous updates to bibliographies may lead to edges that apparently point backward in time. Such "backward" citations seem to constitute less than 1% of the total number of links.
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Many have argued that this way of searching for relevant papers could be improved and made more accurate if citation graphs were incorporated into academic paper search tools. For example, one system was proposed which used both the keyword system and a popularity system based on how many connections
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Analyses of citation graphs have also led to the proposal of the citation graph as a way to identify different communities and research areas within the academic world. It has been found that analysing the citation graph for groups of documents in conjunction with keywords can provide an accurate way
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While citation graphs have had a noticeable impact on several areas of academia, they are likely to become more relevant in the future. As the body of published research grows, more traditional ways of searching for papers will become less effective in narrowing down relevant papers to a particular
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Citation graphs have a history of being used to aid in organising and mapping citations of legal documents. 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
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algorithms on the citation graph. Instead of looking at similarity between two nodes, or clusters of many nodes, this method instead goes through the links between nodes to trace a research idea back to its beginning, and so discover its progression through different papers to where its current
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However, developments like this face similar challenges to that of most applications of citation graphs, which is the face that there is no standardized format or way of citing. This makes the construction of these graphs very difficult, since it requires complex software analysis to extract
452:, and it is found that closeness in the citation graph can predict a level of text-based similarity. 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.
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topic. For example, text-based similarity can only go so far in selecting which papers are relevant to a topic, whereas the addition of citation graphs could make use of giving higher priority to those papers which have a lot of connections to other papers relevant to the topic.
515:, known in this context as a co-authorship network, the nodes are the authors of documents, linked if they have co-authored the same document. The link weights between two authors in co-authorship networks can increase over time if they have further collaboration.
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documents to be found when needed for a court decision. As a way of replacing or improving upon traditional search methods, this citation graph aided way of organising legal documents can provide higher efficiency, accuracy, and organisation.
421:(SCI) in his paper entitled "Networks of Scientific Papers." The links between citing and cited papers became dynamic when the SCI began to be published online. In 1973, Henry Small published his work on co-citation analysis, which became a
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Citation networks are one kind of social network that has been studied quantitatively almost from the moment citation databases first became available. In 1965, Derek J. de Solla Price described the inherent linking characteristic of the
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in papers to return potential matches. 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.
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There are several other types of network graphs that are closely related to citation networks. The co-citation graph is the graph between documents as nodes, where two documents are connected if they share a common citation (see
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citations from papers. One solution proposed to this problem is to create open databases of citation information in a format which could be used by anyone and easily converted to a different form, for example a citation graph.
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In more recent years, visual search tools have been developed which use citation graphs to provide a visual representation of the connections between papers. A commercial implementation of this concept is the search tool
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of citations can be a time-consuming and complicated process. Furthermore, citation errors can occur at any stage of the publishing process. However, there is a long history of creating citation databases, also known as
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Generally the combination of both the in-body citation and the bibliographic entry constitutes what is commonly thought of as a citation (whereas bibliographic entries by themselves are not). References to single,
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As citation links are meant to be permanent, the bulk of a citation graph should be static, and only the leading edge of the graph should change. Exceptions might occur when papers are withdrawn from circulation.
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to identify clusters of similar research. 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
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Bolelli, Levent; Ertekin, Seyda; Giles, C. Lee (2006). "Clustering
Scientific Literature Using Sparse Citation Graph Analysis". In FĂĽrnkranz, Johannes; Scheffer, Tobias; Spiliopoulou, Myra (eds.).
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Similarity analysis is another area of citation analysis which frequently makes uses of citation graphs. The relationship between two papers in the citation graph has been compared to their
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a paper had in the citation graph. In this system, more connected papers were considered more popular and therefore given a higher weighting in the paper recommendation system.
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In principle, each document should have a unique publication date and can only refer to earlier documents. This means that an ideal citation graph is not only directed but
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Lauscher, Anne; Eckert, Kai; Galke, Lukas; Scherp, Ansgar; Rizvi, Syed
Tahseen Raza; Ahmed, Sheraz; Dengel, Andreas; Zumstein, Philipp; Klein, Annette (2018-05-23).
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Yong, Fang; Rousseau, Ronald (2001), "Lattices in citation networks: An investigation into the structure of citation graphs",
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Lu, Wangzhong; Janssen, J.; Milios, E.; Japkowicz, N.; Zhang, Yongzheng (2007), "Node similarity in the citation graph",
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Sadeghian, Ali; Sundaram, Laksshman; Wang, Daisy Zhe; Hamilton, William F.; Branting, Karl; Pfeifer, Craig (June 2018).
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is a reference to a published or unpublished source (not always the original source). More precisely, a citation is an
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1032:; Milios, Evangelos E. (2004), "Characterizing and Mining the Citation Graph of the Computer Science Literature",
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James R Clough; Jamie
Gollings; Tamar V Loach; Tim S Evans (2015). "Transitive reduction of citation networks".
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Informetrics : quantitative methods in library, documentation and information science
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888:"Keywords-Driven and Popularity-Aware Paper Recommendation Based on Undirected Paper Citation Graph"
808:. Lecture Notes in Computer Science. Vol. 4213. Berlin, Heidelberg: Springer. pp. 30–41.
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Citation graphs have been utilised in various ways, including forms of
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Liu, Hanwen; Kou, Huaizhen; Yan, Chao; Qi, Lianyong (2020-04-24).
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assertions in electronic scientific articles are known as
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359:, so there is a lot of information about such problems.
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661:. Morgan & Claypool Publishers.
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