A Visualization of Research Papers
Based on the Topics and Citation Network
Rina Nakazawa, Takayuki Itoh, Takafumi Saito
Ochanomizu University, Tokyo University of Agriculture Technology{leena@itolab.is.ocha.ac.jp, itot@is.ocha.ac.jp, txsaito@cc.tuat.ac.jp}
Abstract
Survey of research papers is not an easy task for
novice researchers, because they are not always good at
finding all appropriate keywords for the survey. Moreover,
it is not easy for them to understand positions of papers intheir research fields instantly, even when they use famous
search engines like Google Scholar; it may often take along time for them to find scholarly literature. On theother hand, many researchers have presented citation
visualization techniques for surveying research papers.
However, it is still often difficult to observe thecomplicated relations across multiple research fields ortraverse the entire relations in their interest. In this paper,
we proposed a visualization technique for citation
networks applying topic-based paper clustering. Our
technique categorizes papers applying LDA (Latent
Dirichlet Allocation), and constructs clustered networks
consisting of the papers.
Keywords--- Citation network visualization, edge
bundling, topic-based.

relations of papers that cover multiple fields. To organize
the open problems, we define the demands in visualizationof citation networks for survey of papers as follows: Categorize papers that have similar topics to thesame group
 Place papers that belong to the category incommon closer
 Place citing and cited papers closer
 Summarize citation relationships
In this paper, we propose a visualization techniquethat satisfies these four demands. Users can follow up
research fields and citations, and finally understand therelations among the related papers using this technique.
Our technique categorizes papers based on their contents
first. Then, it constructs a citation network by treating
papers as nodes, and citations as directed edges. Our
technique visualizes the relations of research papers with
their contents and citations. It would help novice
researchers to understand the differences between thetendencies of similar research fields.

2. Related Work
1. Introduction
Survey of research papers is very important for research
processes, understanding the trend of the research fields
and finding the related work. Researchers use text-based
portal Web sites such as Google Scholar [1] and ACM
Digital Library [2], and look up for the references of
papers they read. However, it is difficult for novice
researchers to survey papers and grasp the position of thepapers in the research fields with search results. Besides,
they may miss papers in case they do not know all theappropriate keywords and in case papers they really wantto survey straddle multiple fields.
There have been many researches on visualization ofcitation networks such as Mackinlay [3] and Small [4]which are useful for survey of research fields. However,
we suppose still there are many open problems on
visualization of citation networks. For example,
researchers continuously trigger for new fusional fields,
and therefore they need to organize and understand theThis section introduces existing visualization techniques
for paper citation networks.
PaperLens [5] is a visualization technique that applies
the mixture distribution model to the titles and keywords,
then estimates their topics, and finally shows papers by
topics and publication years. Brandes et al. [6] presented avisualization technique for citation networks with
topographic maps that places the hub papers cited by many
papers higher. It also arranges the papers that have similar
citation pattern closer. That enables us to easily find the
hub papers and the groups of papers that have similar
citation patterns. Citeology [7] orders papers by the
number of their citations with respect to each year, andplaces them from the center of the display. It can visualize
up to eight generation of the citations. This study
represents structures of citation networks by placing nodes
corresponding to papers in the time-series order. When acitation network has complicated relations across multiple
research fields, it causes serious edge crossing andcluttering which bring bad impact on readability.
Visualization results with heavy cluttering prevent theusers from grasping the positions of papers, while the users
want to understand the positions of the interested papers in
the research fields. Dunne et al. [8] proposed an integrated
visualization of citation network and summary of papers.
The users can look at the citation, ranking based on thecitation count, and summary of papers in the cluster
generated by graph clustering based on citation structure at
the same time. It may require larger display spaces. Also,
the network visualization shows only papers extracted by
the keyword-based search, so the users may miss papers
that are cited by several papers related to the keyword,
because they do not include the particular keyword.
Though these novel visualization techniques havebeen presented, it is not still always easy to find important
papers by using such techniques. One of the reasons is thatthese existing techniques often require users to manuallyspecify the papers whose citation they want to figure out.
It often happens that novice researchers do not know all
the appropriate keywords, and therefore it is not easy forthem to determine which papers they should read. Anotherreason is that many recent new research fields havetriggered as fusions of multiple research fields.
Researchers need to organizationally understand therelations of papers that cover such multiple fields along
their fusion. However, there are few visualization
techniques addressing this problem.

3. Proposed techniqueThis section describes the processing flow of the presented
technique. We treats the papers as nodes, and citations as
directed edges of a network. The technique classifies thepapers based on their contents to construct a hierarchicalnetwork. It then applies our hierarchical network layout
technique with an edge bundling algorithm. Our
implementation also provides rendering and interaction
techniques.

3.1. Paper classification
The proposed technique applies LDA (Latent Dirichlet
Allocation) [9] to categorize papers based on the contentsof papers. LDA is a generative topic model that considers
each document as a mixture of various topics. It could
solve the problem to categorize papers that straddle
multiple research fields. The technique applies LDA to thesets of paper abstracts to estimate topics and calculate thetopic distribution for each abstract. We regard these topics
as research fields and categorize all papers based on them.
The technique supposes a paper is related to theparticular topic, if a value of the topic distribution is larger

than the threshold. We removed unnecessary words fromthe abstract as a preprocessing to improve the quality ofclassification results. The removed words included nonimportant words such as prepositions, or too frequently
used terms such as “propose” and “technique”. Then, we
presumed the contents of the topic from 20 words whose
probability is highest in the topic.

3.2. Network layout
Next, our technique arranges nodes applying a hybrid
force-directed and space-filling graph drawing algorithm[10] to calculate the positions of nodes corresponding to
the individual papers. The technique displays the nodes
supposing that their sizes are proportional to the number ofcitations. The force-directed algorithm enables to place
papers that belong to the same research category closely,
and also, papers that have citation relations closely. Then,the space-filling algorithm enables to avoid the node
cluttering and improve the display space utilization.
After the above process, the technique summarizes
the edges corresponding to citations by applying an edge
bundling algorithm. Our implementation of the edge
bundling enables users to adjust the threshold controllingwhether bundle the edges or not. We have already
implemented the edge bundling algorithm in our previous
work [11]; however, it had a problem that straight bundles
with which summarizes a lot of edges may lead to
misconceptions of Gestalt principle (Figure 1) when thebundles avoid nodes and bend at a right angle.
To prevent the misconceptions, we place nodes incircular and bundle the citation edges with Catmull-Rom
spline curves (Figure 2). Our technique firstly calculates
the shapes of all bundle paths so that they do not overlap
the node clusters. According to the threshold the user sets,
the technique determines whether the number of the edgesof one cluster with the others is larger than the threshold
like Figure2-(2). Then, it bundles the edges only when thenumber of edges between two clusters is larger than thethreshold. (Figure2-(3)). The technique applies this process
to all pairs of the node clusters (Figure2-(4)).

Figure 1 A misconception of Gestalt principle

Figure 2 Edge bundling3.3. Color scaling for network rendering
Since citation networks have directionality so-called“cited” and “citing”, our technique draws the cited side ofthe edges in bright pink, and the citing side of the edges indark pink, to represent the directionality of the edges. We
can also draw arrows or assign different hues to the eachside of the edges for the representation of directionality ofthe edges. However, these representations are not always
adequate for large-scale networks and networks in which
there are many hubs. When we represent the direction byarrows, heavy cluttering may happen around hub nodes ordense regions, which would degrade the readability.
Besides, we assign hues to the nodes, and our techniquecontrols brightness to represent the edge direction. As
Figure 3 shows, we draw nodes with the color scalecorresponding to the publication years.

Figure 3 Color scaling (Upper): The node color,(Lower): The edge color

3.4. User Interface
Figure 4 is a snapshot of the user interface we
implemented. The left side of the window features thedrawing space, while the right side features two tabs. Oneof the tabs features various GUI widgets. Users can scaleand shift the view, switch the edge bundling mode, and set
its threshold, by using the GUI widgets shown in Figure 4(1)(3). When a user clicks a node corresponding to aparticular paper, the technique displays the details of thepaper such as ACM identifier, title, authors, year, andabstract, on the panel featured by the other tab. At thesame time, it highlights the edges of the clicked node, andthose of the nodes that are connected to the clicked node.
This edge highlight function is applicable to two nodes
together, and this enables to compare the citations of each
paper.
By the way, it is not always easy for the novice
researchers to find the paper that they should read first,
just by observing the citation networks. Such users canfilter papers on the display by selecting a research category
or entering a keyword. Selecting a research category thatthe user is interested in, the node cluster that has only the
research category is magnified in the center of the display.
Also, when the user enters a keyword in the text input
widget shown in Figure 4 (2), the technique displays only
the papers whose titles include the keyword. When users
want to survey whole contents of the conference or
research fields, it is useful to firstly overview, and thennarrow down the focus cluster by selecting a category orentering a keyword. They can track bundles of the focus
cluster, and then move to focus on other clusters.
In case that users want to look into respective papers,
they can also narrow down the focus paper in the sameprocedure. If they click a paper node, its citation edges arehighlighted. They can follow these edges and trace them.

4. Results
We implemented the proposed technique with Java
Development Kit (JDK) 1.6.0. We applied a citation
network dataset consisting of 1072 full papers published in
the SIGGRAPH conferences during 1990 to 1994, andduring 2000 to 2010, provided by the ACM Digital Library[2]. We extracted the title, publication year, abstract,references, and authors from html files of the papers. We
did not apply the paper information during 1995 to 1999,
because we could not extract the abstracts from ACM
Digital Library.

Figure 4 User Interface

4.1. Example of image processing
Figure 5 shows a visualization result when a user selected
the "image processing" category. The cluster containing
the papers categorized only to "image processing"appeared in the center of the view. Most of nodes of thecluster are warm colored. This denotes the tendency thatmost of papers on image processing are published by
SIGGRAPH after 2000.

and “shape modeling” refer to the papers in the “hardwareand GPU” cluster, and the researches in these fields haveoften evolved based on the researches in the “hardware andGPU” category. Especially, the relation between the"hardware and GPU" and "lighting" clusters clearly shows
the above fact. Therefore, we expect that the "hardwareand GPU" cluster could give a clue to the research teamthat develops hardware systems when they want to know
which research fields their products are well applied.

Figure 5 Example of image processing

4.2. Example of hardware
Suppose that a user surveys for research papers on“hardware and GPU.” Figure 6 is an example when theuser selected the "hardware and GPU" category. We could
observe that the cluster in the center contained papers
categorized only to "hardware and GPU" had dense
relationships between the "physical simulation", "lighting",and "shape modeling" categories. We also found that The
cited bundles of the “hardware and GPU” cluster are
thicker than the citing ones, which means many papers inthese research fields, “physical simulation”, “lighting”,

Figure 6 Example of hardware

4.3. Example of lighting and CG algorithm
Next, we supposed that a user searched for papers related
to lighting. Figure 7 shows an example of visualization
under this supposition. The cluster A is a group thatcategorized into “lighting and CG (Computer Graphics)
algorithm”. We found the nodes in this cluster werecolored in light blue or yellow-green, where the colors
depicted that the papers corresponding to the nodes were
published in 1994 and 2000. Although this cluster is small,the problems in this research field were addressed once in1994 and discussed again in 2000.
The papers in the cluster A are as follows: A fast shadow algorithm for area light sources usingback projection (in 1994)
 The irradiance Jacobian for partially occluded
polyhedral sources (in 1994)
 A clustering algorithm for radiosity in complex
environments (in 1994)
 Illuminating micro geometry based on precomputed
visibility (in 2000)

Efficient image-based methods for rendering soft
shadows (in 2000)
 Conservative volumetric visibility with occluder
fusion (in 2000)
We especially suppose the above papers published in2000 might have triggered the invention of PRT(Precomputed Radiance Transfer).

Figure 8 Pictures in papers of the green stream

We listed the papers that belong to the blue stream.
Image-based skin color and texture analysis/synthesisby extracting hemoglobin and melanin information in
the skin (in 2003)
Analysis of human faces using a measurement-basedskin reflectance model (in 2006)

Figure 9 Pictures in papers of the blue stream

Figure 7 Example of lighting and CG algorithm

As we could understand from these pictures of thepapers, our technique demonstrated that researches of
SIGGRAPH related to “skin” could be divided into twogroups, based on their topics and citations. One of the
topics is related to human animation generation using
motion capture systems, and the other discusses generationor analysis of human face skins.
This result demonstrates that the technique enables thenovice researchers, who study computer graphics and want
to read papers related to skin, to understand that there are
two research fields related to skin and to choose which
field they should survey.

4.4. Example with a keyword
Figure 10 (Upper) shows an example that a user entered
the keyword "skin". When the user did not apply the edge
bundling and clicked the two orange nodes, many edges
are drawn as shown in Figure 10 (Lower). The techniquehighlights the edges connected to the clicked nodes and thecitations of the cited and citing nodes.
Figure 10 (Upper) demonstrates that we can classify
the research papers whose titles contain the term "skin"into two research fields. Therefore, we clicked two orange
nodes, one in a larger cluster, and the other categorized in
the different cluster far from the first one. As a result, we
could grasp the two streams containing each of the clicked
nodes, because all the displayed nodes in Figure 10(Lower) connect with either blue or green edges.
We listed all the titles and figures (see Figures 8 and
9) of the papers classified into these two groups. The
papers connected with green edges as follows:Continuous capture of skin deformation (in 2003)

Building efficient, accurate character skins fromexamples (in 2003)
Capturing and animating skin deformation in humanmotion (in 2006)
Data-driven modeling of skin and muscle
deformation (in 2008)

Figure 10 (Upper) Result with a keyword “skin”,(Lower) Result when an user click two nodes

5. Evaluation

5.2. Evaluation: comparison with time-oriented
visual representation

5.1. Preliminary questionnaires
There have been a lot of citation visualization techniques
as we mentioned in Section 2. As against our techniqueapplies a general purpose graph layout technique, typicalexisting techniques places nodes corresponding to thepapers in time-ordered. However, we assume the timeordered layout policy is not mandatory, because it issufficient in many use cases to recognize each of thevisualized papers are old or new. For example, we often
just want to know whether the paper is the oldest one as
the roots in the research field, or the newest one. To prove
our hypothesis, we conducted the subjective evaluation tocompare our technique and the time-oriented visualization
technique.
Before the evaluation, we had a questionnaire todefine what we carefully observe while surveying papers.
We asked three questions to ten graduated students
majoring computer science.
1.
What do you want to know when you search forpapers?
2.
What technique do you want for surveying papers
well?
3.
What do you want to know if you look into thecitation network visualization in a particularconference for twenty years?
Regarding the question 1, a half of the students
answered that they would like to know whether the papers
are similar to their researches. In other words, it isimportant to define criteria of similarity of research topics
and papers. Other answers are regarding citations andresearch topics or fields of papers. These answers suggests
the usefulness of visualizing topic-based structures of
papers and citations. We also suppose the structures of
topics and citations can be used to determine the similarity
among papers. Several students answered they wanted toknow the differences (e.g. advantages and disadvantages)
among the techniques presented in the papers. We would
like to solve this issue as a future work because both ourtechnique and the existing techniques cannot represent theconcrete contents only as the visualization results.
Regarding the question 2, more than half of thestudents mentioned that word-based smart searchtechniques are important for paper survey processes,
including synonym recommendation and searchrefinement. This result proves that novice researchers
including graduated students had troubles while selecting
keywords to search papers.
Regarding the question 3, we roughly divide theanswers into three categories, “the transition of research
fields”, “the citation relationships”, and “both research
fields and citation relations, or what they reveal in
combination”. It demonstrates the demands to understandboth research fields and citations.

According to the result of the questionnaire, we asked 21
graduate students majoring computer science to compare
our technique shown in Figure 11 (called “A” in thissection) with the time-oriented citation visualization
shown in Figure 12 (called “B” in this section), andevaluate which visualization is proper to know the contents
below. We implemented the time-oriented techniquemimicking Citeology. We asked participants to answer thequestions as 5-level scores, where 1 was a strong
agreement with A, and 5 was a strong agreement with B.
The following are questions we asked to participants:
1.
The transition of papers amount published in theconference every year.
2.
The main topic in the conference.
3.
The trend of a research topic by year.
4.
The research fields that seem to have a strong
relationship with a field you focus on.
5.
Much-cited papers on a certain topic.
6.
The latest paper on a certain topic.
7.
The content trends of papers citing the paper you
read (or clicked).
8.
Papers whose contents are similar to the paper you
read (or clicked).
9.
Papers that had a great influence on the paper you
read (or clicked).

Figure 11 Our technique (A)Figure 12 Time-oriented technique (B)Figure 13 shows the evaluation result. The X-axis
denotes the sequential number of questions, and the Y-axis
denotes the quantity of responses. Our technique wasevaluated as more beneficial in the questions 2, 4, 5, 7, and8, while the time-oriented visualization B was evaluated as
more effective in the questions 1, 6, and 9.
Although we expected the time-oriented technique B
has an advantage on the questions 3 and 9, the figure
demonstrates their rates varied widely. The result denotes
our technique are also effective for the questions 3 and 9.
Especially, the rate of the questions 9 resulted in thevariation because we did not need to know the publication
year strictly to distinguish papers that had the great
influence. This result proves that we do not need to assign
the publication year to the X-axis of the display space.

Figure 13 Result of the evaluation

Conclusions
We presented a visualization technique of citation
networks for survey of research papers, and discussed theresults. Our technique applies topic-based paper clusteringto construct hierarchical network. It then applies a hybrid
force-directed and space-filling network layout algorithm,
and an edge bundling technique with Catmull-Rom spline
curve. This paper also introduced the results and the user
evaluation.
As a future work, we would like to apply the techniqueto larger citation datasets and conduct user evaluations.
Also, we would like to also visualize more complex
datasets combining co-citation and co-author networks.

We expect it helps to understand the citation relations
across multiple research fields more easily.

References
[1]
[2]
[3]

Google Scholar, http://scholar.google.co.jp/
ACM Digital Library, http://dl.acm.org/
J. D. Mackinlay, R. Rao, S. K. Card, An organic userinterface for searching citation links, the SIGCHI
conference on Human factors in computing systems, pp.
67-73, 1995.
[4] H. Small, Visualizing science by citation mapping, Journalof the American society for Information Science, 50(9),799-813, 1999.
[5] B. Lee, M. Czerwinski, G. Robertson, B.B. Bederson,
Understanding research trends in conferences using
PaperLens, CHI'05 extended abstracts on Human factors incomputing systems (pp. 1969-1972), 2005.
[6] U. Brandes, T. Willhalm, Visualization of bibliographic
networks with a reshaped landscape metaphor, thesymposium on Data Visualisation 2002.
[7] J. Matejka, T. Grossman, and G. Fitzmaurice, Citeology:
visualizing paper genealogy, CHI'12 Extended Abstracts
on Human Factors in Computing Systems, pp. 181-190,
2012.
[8] C. Dunne, B. Shneiderman, R. Gove, J. Klavans, B. Dorr,
Rapid understanding of scientific paper collections:Integrating statistics, text analytics, and visualization,
Journal of the American Society for Information Scienceand Technology, 63(12), 2351-2369, 2012.
[9] D. M. Blei, A. Y. Ng, and M. I. Jordan, Latent Dirichlet
Allocation, Journal of Machine Learning Research, Vol. 3,
pp. 993-1022, 2003.
[10] T. Itoh, C. Muelder, K. Ma, J. Sese, A Hybrid SpaceFilling and Force-Directed Layout Method for Visualizing
Multiple-Category Graphs, IEEE Pacific Visualization
Symposium, 121-128, 2009．
[11] R. Nakazawa, T. Itoh, J. Sese, A. Terada, Integrated
Visualization of Gene Network and Ontology Applying a
Hierarchical Graph Visualization Technique, 16th
International Conference on Information Visualization,2012