Tracing citation links helps retrieve related publications. While most tools only allow the user to follow the citations of a single publication, some approaches support jointly analyzing the citations of a set of publications. Along similar lines, PURE suggest provides a detailed visual explanation of the ranking of suggested publications. The ranking is based on a score that combines citation numbers with keyword matching and is shown as a glyph for each publication. A citation network component references this glyph and visually embeds it into a timeline and cluster visualization

Beck, Fabian

Krause, Cedric

English

Forschungsinformationssystem der Universität Bamberg

EUROVIS 2022/ S. Lenti, M. Krone, and J. Schmidt PosterVisually Explaining Publication Ranks inCitation-based Literature Search with PURE suggestFabian Beck and Cedric KrauseUniversity of Bamberg, GermanyA BFigure 1: PURE suggest allows selecting publications (A, left) and then suggests related publications (A, right), while visualizing the citationnetwork (A, bottom). A full-feature interface for small screens is also available (B).AbstractTracing citation links helps retrieve related publications. While most tools only allow the user to follow the citations of a singlepublication, some approaches support jointly analyzing the citations of a set of publications. Along similar lines, PURE suggestprovides a detailed visual explanation of the ranking of suggested publications. The ranking is based on a score that combinescitation numbers with keyword matching and is shown as a glyph for each publication. A citation network component referencesthis glyph and visually embeds it into a timeline and cluster visualization.1. IntroductionA citation represents a semantic link between two publications.When a set of publications already defines a research focus, andmultiple citation links to or from these seed publications referencethe same other paper, then, this work is likely also of high relevancefor the given focus. Iteratively applying this idea now allows inter-actively building a coherent body of references through a small setof seed publications, for instance, to assemble a section of relatedwork for a publication. We refer to this concept as citation-basedliterature search.Traditional search interfaces for academic literature, like GoogleScholar, only partly support this approach—they only allow inves-tigating the citations from and to a single selected paper. How-ever, there exist specialized citation-based search tools, like Cita-© 2022 The Author(s)Eurographics Proceedings © 2022 The Eurographics Association.DOI: 10.2312/evp.20221110 https://diglib.eg.orghttps://www.eg.orgF. Beck & C. Krause / Visually Explaining Publication Rankstion Gecko and Research Rabbit, that work with sets of selectedseed papers to inform their suggestions as discussed. They also vi-sualize the citations in the network and prioritize suggestions, butdo not explain their prioritization or allow the user to steer it. Re-lated techniques described in research literature may also visualizecitation links, but have a different focus (cf. Section 3).For efficiently deciding whether a publication is relevant, it iskey to understand why it is suggested, specifically:Q1: Is the publication linked by incoming or outgoing citations?Q2: Is the publication covering a certain topic of interest?Q3: Does the publication have special (citation) characteristics?Q4: How does the publication relate to the selected publications?Focusing on these questions, we have developed PURE suggestas shown in Figure 1. It computes and visually explains a score torank the suggested publications based on incoming and outgoingcitations links (Q1), as well as on a boost degree to consider thetopics of interest (Q2). Using citation statistics and other indicators,certain characteristics (highly cited, literature survey, and new) of apaper can be inferred, which are then used as tags for the respectivepublications (Q3). A citation network visualization and interactivehighlighting support exploring the relations between suggested andselected seed publications (Q4).PURE suggest is implemented as a web application using Vue.jsand D3.js. Citation and publication meta-data is retrieved throughpublic APIs of OpenCitations and Crossref. The interface is splitinto three sections: the selected seed publications on the left, thesuggested publications on the right, and the citation network at thebottom (cf. Figure 1A). On mobile screens or when resizing thewindow to smaller width, the layout switches to a vertical scrollinglayout (cf. Figure 1B), with quick access buttons at the bottom tojump between the three sections; this supports using the tool alsowithout window switching aside an enlarged document. The imple-mented tool is publicly available at https://fabian-beck.github.io/pure-suggest/ and published as open source on GitHub; this paperdescribes the tool in version v0.2.1.†2. Visually Explaining Publication RanksThe publications are ranked by a score s, both to order the selectedpublications and to prioritize suggestions. The score is based on thenumber of outgoing (citing, o) and incoming (cited by, i) citationlinks referencing the set of currently selected publications. Whilethe sum (o+ i) is a good basis already, a search can even better besteered when having the option to boost certain keywords. Givena user-specified set of keywords, we compute boost degree b forevery publication as the number of distinct matched keywords inthe title. To make a significant difference in the score, we raise 2 tothe power of b as a factor for the final score s = (o+ i) ·2b.Throughout the interface, we consistently use cyan-green fordiscerning selected publications from suggested ones colored inblue , while information related to the boost keywords is shownin yellow . In both the selected and suggested publications, eachpublication carries a rectangular glyph providing the ranking score† https://github.com/fabian-beck/pure-suggest/releases/tag/v0.2.1as a number (large number at the top), encoded also as backgroundcolor from white to gray. The glyph further visually explains thescore, showing the number of outgoing citation links o on the lowerleft (e.g., ) and the number of incoming ones i on the lower right(e.g., ); the boost degree is visually indicated by a chevron iconand a yellow circle, growing from one chevron and a small circlefor b = 1 to three chevrons and a larger circle for b ≥ 3 (e.g., forb = 2). Hovering (desktop version) or clicking (mobile version) theglyph also displays a tooltip with a textual explanation. On click, apublication gets activated, and further details are shown, such as thefull reference with general citation counts and external links. Boostkeywords are marked with a thick yellow underline if matched inthe title. Tags (e.g., ) appear if the publication matchesthe respective heuristic; they are explained in tooltips on demand.The citation network visualization at the bottom visually refer-ences the publication glyphs in a simplified version, using the samecolors and shapes, as well as displaying the overall score as a la-bel. The visualization can be switched between a timeline and clus-ter mode, both implemented based on the force-directed layout ofD3.js with adapted forces (e.g., to place publications at the correctx-position on the timeline). Publications can also be activated viaclicking in the visualization and are then highlighted in the respec-tive list of publications as well. Citation links of the currently ac-tive publication are shown as dashed lines in the visualization, andlinked publications are drawn with a thicker border in all views.3. Application ExampleAs a demonstration, we used PURE suggest to collect related ap-proaches for this work. We started our literature search from threerelevant seed papers known to the authors [BKW16, HHKE16,HPLC19]. We set the keywords cit and vis to focus the search onvisualizations of citation data. We summarize the application ex-ample and detected relevant publications in the following; the com-plete process is described in our supplemental material.We employed different strategies. Oftentimes, we considered thesuggested publications with the highest score. They mostly con-cerned the long-term evolution of research areas [Che06, Che99,ET07] and approaches that visualize author and citation networksfor a single publication [GBP21, DSG∗12]. Finding publicationswith balanced high values for incoming and outgoing citations inthe timeline view (Q1) yielded a paper that addresses the previ-ously missing aspect of citation context [BMS17]. By inspectingfurther titles of high-ranked publications, we identified the field ofpublication ranking [ZSHL18]. This topic is relevant, but we didnot identify other papers in that direction when using the keywordsscor (stem of score/scoring) and rank (Q2). Analyzing connec-tions of single publications in the cluster view (Q4) led to the rele-vant integration of keyword-based literature search, which was ex-tended through the usage of the keyword keyword [IIS∗17,AYA22](Q2). Lastly, adding related publications tagged as literature sur-veys [LTW∗18,FHKM17] (Q3) resulted in many new suggestions.This led to the addition of the only other publication we found thatconcerned the iterative buildup of a literature selection, but focuseson augmenting search engine results [SH21] (highlighted in Fig-ure 1). We conclude that our combination of the above aspects ad-dresses a gap in research literature.© 2022 The Author(s)Eurographics Proceedings © 2022 The Eurographics Association.20F. Beck & C. Krause / Visually Explaining Publication RanksReferences[AYA22] ALMUHANNA A. A., YAFOOZ W. M. S., ALSAEEDI A.: Aninteractive scholarly collaborative network based on academic relation-ships and research collaborations. Applied Sciences 12, 2 (2022), 915.doi:10.3390/app12020915. 2[BKW16] BECK F., KOCH S., WEISKOPF D.: Visual analysis and dis-semination of scientific literature collections with SurVis. IEEE Trans-actions on Visualization and Computer Graphics 22, 1 (2016), 180–189.doi:10.1109/tvcg.2015.2467757. 2[BMS17] BERGER M., MCDONOUGH K., SEVERSKY L. M.: cite2vec:Citation-driven document exploration via word embeddings. IEEETransactions on Visualization and Computer Graphics 23, 1 (2017),691–700. doi:10.1109/tvcg.2016.2598667. 2[Che99] CHEN C.: Visualising semantic spaces and author co-citationnetworks in digital libraries. Information Processing & Management 35,3 (1999), 401–420. doi:10.1016/s0306-4573(98)00068-5. 2[Che06] CHEN C.: CiteSpace II: Detecting and visualizing emergingtrends and transient patterns in scientific literature. Journal of the Amer-ican Society for Information Science and Technology 57, 3 (2006), 359–377. doi:10.1002/asi.20317. 2[DSG∗12] DUNNE C., SHNEIDERMAN B., GOVE R., KLAVANS J.,DORR B.: Rapid understanding of scientific paper collections: Integrat-ing statistics, text analytics, and visualization. Journal of the AmericanSociety for Information Science and Technology 63, 12 (2012), 2351–2369. doi:10.1002/asi.22652. 2[ET07] ELMQVIST N., TSIGAS P.: CiteWiz: A tool for the visualizationof scientific citation networks. Information Visualization 6, 3 (2007),215–232. doi:10.1057/palgrave.ivs.9500156. 2[FHKM17] FEDERICO P., HEIMERL F., KOCH S., MIKSCH S.: A surveyon visual approaches for analyzing scientific literature and patents. IEEETransactions on Visualization and Computer Graphics 23, 9 (2017),2179–2198. doi:10.1109/tvcg.2016.2610422. 2[GBP21] GUILARTE O. F., BARBOSA S. D. 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IEEE Transactions on Visualization and Computer Graphics 23,1 (2017), 771–780. doi:10.1109/tvcg.2016.2598827. 2[LTW∗18] LIU J., TANG T., WANG W., XU B., KONG X., XIA F.: Asurvey of scholarly data visualization. IEEE Access 6 (2018), 19205–19221. doi:10.1109/access.2018.2815030. 2[SH21] SHUKLA S., HOEBER O.: Visually linked keywords to supportexploratory browsing. In Proceedings of the 2021 Conference on HumanInformation Interaction and Retrieval (2021), ACM, p. 273–277. doi:10.1145/3406522.3446037. 2[ZSHL18] ZHOU Z., SHI C., HU M., LIU Y.: Visual ranking of academicinfluence via paper citation. Journal of Visual Languages & Computing48 (2018), 134–143. doi:10.1016/j.jvlc.2018.08.007. 2© 2022 The Author(s)Eurographics Proceedings © 2022 The Eurographics Association.21

Visually Explaining Publication Ranks in Citation-based Literature Search with PURE Suggest

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Visually Explaining Publication Ranks in Citation-based Literature Search with PURE Suggest

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