Exploration of Researchers’ Social Network for
Discovering Communities
Ryutaro Ichise1 , Hideaki Takeda1 , and Kosuke Ueyama2
1

National Institute of Informatics,
2-1-2 Hitotsubashi Chiyoda-ku, Tokyo 101-8430, Japan{ichise, takeda}@nii.ac.jp
2
TRIAX Inc.
4-18-2-203 Takadanobaba, Shinjyuku-ku, Tokyo 169-0075, Japan
ko@triax.jp

Abstract. The research community plays a very important role in helping researchers undertake new research topics. The authors propose a community mining system that helps to find communities of researchers by using bibliography
data. The basic concept of this system is to provide interactive visualizationof communities both local and global communities. We implemented this concept using actual bibliography data and present a case study using the proposedsystem.

1 Introduction
Our modern life has become fundamentally supported by various technical systems,
ranging from traditional systems like roads and bridges to highly technological systems
like nuclear power plants, all of which have been built and thus maintained with scientific and technical knowledge. Sustaining our modern life is dependent on maintenanceof these systems. Accordingly, to avoid fatal failure of such systems, preserving thisscientific and technical knowledge is vital.
Scientific and technical knowledge is kept explicitly by means such as publishedpapers, but that it is not the only way. The crucial part of knowledge is kept implicitly among communities of scientists and engineers. Not only papers themselves but
also communities behind the papers play an important role in keeping knowledge fromgeneration to generation. In this paper, we discuss how research communities can be
discovered from bibliographic information. In particular, we do not aim at an automatic
discovery system but an interactive system with people, i.e., users can browse various
types and sizes of communities.
This paper is organized as follows. In the following section, we clarify community
discovery with related studies. In Section 3, we discuss the proposed design for a community mining system. In Section 4, we describe an implemented community mining
system based on the design policies discussed in Section 3. In Section 5, we explain
the proposed system using a number of examples. Finally, in Section 6, we discuss ourconclusions and areas for future study.
T. Washio et al. (Eds.): JSAI 2005 Workshops, LNAI 4012, pp. 458–469, 2006.
c Springer-Verlag Berlin Heidelberg 2006Exploration of Researchers’ Social Network for Discovering Communities

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2 Discovery of Research Communities
We can obtain useful and reliable information from communities. Most researchers do
not include all of the techniques used in their experiments when writing a paper because some of them, such as computer coding, cannot be represented in words, such as
computer coding techniques. Such techniques propagating within a local research community, however, may be useful for developing new technologies and learning about
existing technologies.
To clarify existing research communities, bibliographic information is widely used.
In co-citation analysis [1], all the papers that are cited in a paper make up a community
for a certain research area. However, this paper-based analysis overlooks the characteristics of individual researchers. As a result, realizing that each researcher has an individual research area is difficult. On the other hand, CiteSeer [2] and Google Scholar [3] areable to handle research communities from a micro viewpoint because they handle coauthor and citation information from bibliographies and use the information for individual researchers. Although these systems are good for finding micro communities, they
are not suitable for finding the position of a researcher within a global research community. In the present paper, we propose a community mining system for researchersthat has both local and global viewpoints. The proposed system will facilitate understa!
nding of the researcher community and will advance new areas of research.

3 System Design
3.1 Relationships for Communities
The most important information for finding communities of researchers is containedin the bibliography. In scientific network research, several relationships can be obtained from this information. The relationships in the knowledge domain include coauthorship [4], citation [5] and co-citation [1]. In the present paper, we use the following
three relationships in the knowledge domain to find research communities:– co-authorship– citation– author citation
The relationships are shown in Figure 1. The first two relationships are well known
by knowledge domain researchers. With respect to co-authorship, if we consider the researcher as a node and co-authorship as an arc, we can obtain networks of researchers.
We can consider that researchers whose nodes are linked have the same research interests. With respect to citation, if we can consider the research paper as a node andcitations in papers as arcs, we can obtain networks of papers. The final relationshipis author citation. This relationship also represents relationships among authors. Research papers include citations of papers, as described above. This citation indicatesthat both papers (which have a citation relationship) are related to the same subject. In
the same manner, the authors of both papers have the same research interests. In thepresent study, these relationships are referred to as ”author citations” and are used forcommunity mining.

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R. Ichise, H. Takeda, and K. Ueyama

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EQCWVJQTUJKRYTKVKPIYTKVKPICWVJQTEKVCVKQP
Fig. 1. Three relationships in the knowledge domain

3.2 Community Mining
In this section, we describe the community mining method for the networks describedin the previous section. We use a policy for community mining borrowed from theactive mining approach [6]. The basic concept of active mining is to utilize interactions
between the user and the computer. We separate the mining method into two steps. The
first step is to automatically mine communities by computer, and the second step is
to visualize the information in order to facilitate the user’s understanding of it. After
viewing the communities that are discovered by the computer, the user can specify acommand to find more refined communities. As described above, the proposed method
can facilitate the finding of communities through interactive manipulation of the mining
result by computer.
Several types of indexes have been proposed for finding communities [7]. In thepresent study, we used the following indexes for finding communities.
1. Simple weight
2. Maximum flow
3. Closeness
The first index, simple weight, is a measure for using weight on arcs such as representing the number of co-authors. When the weight is small, the arc is considered torepresent an unimportant relationship. Therefore, highly weighted arcs represent communities and thus can be used as an index for the community. The second index, maxflow, is a measure that focuses on the connection between two different nodes. This
index considers the weight on an arc as the thickness of a pipe and measures the connection between the two nodes. The index is able to calculate the distance between two
nodes that are not directly connected. When the two nodes have a thick connection,even though they have relay nodes, the index may have a high value. This index may
therefore be a good measure for finding communities. The third index, closeness, is ameasure that is used to calculate the distance between two nodes. This index represents
the shortest distance between the two nodes. Therefore, when the distance is small, thetwo nodes are considered to be in the same community.

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4 Research Community Mining System
4.1 System Architecture
We implemented a community mining system using the policies presented in the previous section. The components of the system are shown in Figure 2. The system has two
databases, the CiNii (Citation Information by NII) [8] database and an experimental
database, and five program units. The CiNii database will be described in detail later
herein.
The database generation unit in Figure 2 selects records from the CiNii database andsends them to the database management unit. MySQL [9] is used as the database management unit. If possible, the mining index discussed in Section 3.2 is then calculatedby the mining index calculation unit. The components are written in Perl. Note that theentire mining index cannot be calculated at this stage because of the need for a user
query. The result of the calculated index is also stored in the experimental database,which is handled by the database management unit.
Mining index
calculation unit

I/O control unit
Database
generation unit

Databasemanagement unit

CiNii
Database

Experimetal
Dabatase

Visualization
control unit

Fig. 2. System architecture

Users access the system through the visualization control unit, which is constructedusing a web browser that includes Flash Player and SVG Viewer. When a user inputs
data from the web browser, the data will be sent to the I/O control unit via the Internet.
The I/O control unit, which is constructed by a web server including the CGI program,generates data for visualization from the user input. The components are also written in
Perl. The I/O control unit calls the database management unit to retrieve data and calls
the mining index calculation unit to calculate the mining index.
In the remainder of this section, the CiNii database, the preprocessing method for
data, and the visualization method are discussed in detail.
4.2 Bibliography Database
The CiNii database is used to obtain bibliographic information. The database is described by approximately 320 megabytes of SGML data. The CiNii database contains

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R. Ichise, H. Takeda, and K. Ueyama

bibliography entries, such as title, author, and year of publication. The number of data
are listed in Table 1. The number of database records in CiNii denotes the number ofrecords in the original database, as described in SGML format. The ”Paper” and ”Researcher” entries denote the number of records for papers and the number of records forresearchers, respectively. The number of researchers is less than the number of papers,
because one researcher can write more than one paper. The ”Author” entries in Table 1
denote the number of authors for each paper. For example, the record is counted as threewhen three researchers write a paper corroboratively. The ”Co-author” entries denotethe number of combinations of authors for a paper. For example, when a paper is written
by four authors, it is! counted as 4 C2 = 6 for the paper. The ”Citation(Paper)” entries
denote the number of citations of a paper. Although one paper usually cites a numberof other papers, the ”Citation(Paper)” entries are lower than the Paper entries because
the CiNii database contains part of the citation information. The ”Citation(Author)”
entries denote the number of researchers who wrote cited papers, with duplication.
Comparing the Paper and Researcher entries reveals that the number of researchers
is less than the number of papers. In contrast, comparing the Citation(Paper) and Citation(Author) entries, the number of Citation(Author) entries is larger than the numberof Citation(Paper), which implies that writers prefer citing various papers to citing several papers written by the same author. Note that the database used in the present study
was created in October of 2003, so it is different from the current CiNii database.
Table 1. Number of data
Number of records in CiNii Number of records in Experimental Database(×1, 000)(×1, 000)
Paper
544
128
Researcher
224
90
Author787
358
Co-author
1103
231
Citation(Paper)
445
36
Citation(Author)
1562
349

4.3 Preprocessing of Database
To construct the experimental database, the data discussed in the previous section was
preprocessed by a database generation unit. In this process, a few attributes are selected from the CiNii database, because most of the attributes, such as ISSN number,included in the CiNii database are not useful in the present experiment. In addition, thedatabase generation unit conducts record linkage for author records. It has heuristics
for dividing author records, because the author records in the CiNii database sometimes have multiple author names in a single author field. For treating such records,
the database generation unit divides long author names using special characters, suchas . In addition, a number of other small record linkage techniques are also used in thisstage.
After the preprocessing has been completed, the experimental database is created by
the database management unit. The numbers of records for the experimental database

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are listed in Table 1. The experimental database contains much less data than the original CiNii database because only the bibliographies of the papers included in the CiNii
database as original information were used. Although the CiNii database contains cited
paper information, the information is not complete and contains several mistakes; it wastherefore not used in the present experiment.
4.4 Visualization
The visualization control unit creates a visualization screen for finding communities
easily. For a user to find a community, they must be able to browse the data interactively. For this purpose, we used a web browser, which includes Flash Player and SVG
Viewer, as a visualization control unit. Data for this unit was provided by a web serveron the Internet. The server is referred to as the I/O control unit. We used two types ofvisualization data provided by the I/O control unit: global visualization by SVG [10]and local visualization by Flash. Global visualization by SVG facilitates the locationof macro communities by showing global relationships on the graph, whereas local
visualization by Flash facilitates the location of micro communities by showing the local relationships near the individual. Both of these methods will be discussed in thissection.
We first discuss graph visualization by SVG. As discussed in Section 3.1, representing the relationships between communities in the form of a graph will facilitate thefinding of communities. We therefore use a graphic representation scheme by SVG,an XML format for representing graphs. The SVG file can be visualized by an SVG
viewer. An example of a SVG graph visualization of relationships among researchers
is shown in Figure 3. However, using such a graphical representation is not suitable forfinding communities in the graph, because most of the nodes will be connected [11].
As a result, the graph could be very large and the user might not be able to find communities in the graph. To discern communities from a large graph, the system shouldhave a function that shows the parts of the graph that may contain the desired communities. Accordingly, we herein propose interactive mining in conjunction with theindexes presented in Section 3.2. When the user specifies an index and its threshold, thesystem automatically divides the graph. This is very important for finding communities because it is hard to specify an index to match the purpose of the user. Although thecurrent implementation of the mining index calculation unit calculates the three indexes
presented in Section 3.2, the I/O control unit can only create an SVG graph for simple
weight.
Next, we will discuss local representation by Flash. After finding a community via
visualization by SVG, we must learn more about each researcher in order to refine thecommunities. For this purpose, users require not only a global point of view with respect
to the communities, but also a local point of view that focuses on each researcher. We
propose visualizing local communities, to which a specified researcher belongs, in order to facilitate the refinement of communities. Locally, a specified researcher is locatedat the center of a field, and related researchers are placed around the circumference of
the initially specified researcher. This visualization is suitable for finding communities
that are built around a researcher. Since we focused on communities of researchers, thecurrent implementation includes only the relationships of co-author and author citation,

464

R. Ichise, H. Takeda, and K. Ueyama

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Fig. 3. An example of graph representation by SVG

Fig. 4. An example of a local viewExploration of Researchers’ Social Network for Discovering Communities

465

as discussed in Section 3.1. An example of the local point of view is shown in Figure 4.
The circle in the center represents the currently specified researcher of interest. The
surrounding circles represent researchers who! are related to the initially specified researcher. Finally, the outermost ring of circles represents researchers who are related to
the researchers of the inner ring. The size of each circle represents the number of papers
that were written by the researcher. The thickness of the line represents the strength of
the relationships.
The user can also perform the following operations to find communities interactively.
– Change the researcher of focus by specifying another researcher.
– Move circles to find relationships among particular researchers.
– Change the type of relationships.
– Show the bibliography of the researcher.
4.5 Other Functions
In addition, the following basic functions for facilitating the finding of communities by
the user are implemented.
– Paper search: The user can search for a paper or researcher by specifying authorname or paper title. This function helps to understand the meaning within discovered communities and helps to find similar communities.
– Ranking: The user can easily find dense communities by using ranking for coauthor or citation. In addition, this function also helps to show the starting point forcommunity mining.

5 Case Study of Community Mining System
In order to demonstrate how the proposed system works, we will discuss the systemusing actual examples. Figure 5 illustrates the start screen of the proposed system. From
this screen, any of the functions of the proposed system can be easily chosen.
From the center of the start screen, we can access the local view by Flash, as discussed in Figure 4. By clicking the center of the start screen, a search window is opened.
After the user inputs the name of a researcher, whom the user wants to know more about,a visualized screen representing the local communities for that researcher is shown. For
example, if we input ”Takeda” (in Japanese) and choose ”Hideaki Takeda” from theTakeda list, the screen shown in Figure 6 will be displayed. The list at the bottom-leftcorner of the screen is a list of authors. When ”Hideaki Takeda” is selected from thislist, a circle denoting ”Hideaki Takeda” is shown in the center of the screen. The circles around ”Takeda” denote researches who have co-authored papers with him. To findcommunities that are built around a specified researcher, the user can visualize the researchers related to a specified researcher. In addition, the system has a function foreliminating relationships that do not exceed a particular threshold specified by the user,
as well as a function for changing the scale or location of circ! les in order to moreclearly display the desired information. Although Figure 6 illustrates the co-author relationships, the proposed system also has the ability to show the author citation relationships in different colors. The bottom-right window shows the bibliography of theresearcher at the center of the screen and is used to determine the field of the researcher.

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R. Ichise, H. Takeda, and K. Ueyama

Fig. 5. Start screen of system

The two items in the box at the bottom-left, shown in Figure 5, are used for searchingfor authors and papers. The two items in the center are used to show rankings. Theserankings include researchers with whom papers have been written and papers that arecited frequently. This function is used for obtaining knowledge for community mining.
The three items in the box at the bottom-right, shown in Figure 5, are used for visualizing the graphs for community mining. These items are used to show citation relationships, author citation relationships, and co-author relationships. Since each of thesefunctions is similar, we will show only the case of the author citation relationships.
When the user clicks the area for the author citation in Figure 5, the screen shown in
Figure 7 is displayed. From this screen, the threshold of the weight of the relationships
can be set. In this example, as shown on the center-left side of the screen, the weightof the relationships is set to 1. In other words, the graph is created using relationships
in which the two researchers have at least one author citation relationship. As a result,
we can divide the researchers into a number of communities. In this case, we can obtain, for example, a community constructed with 30,536 researchers or a community
constructed with 26 researchers. The relationships between community size, numberof communities and threshold are shown in Figure 8. The list of the communities isshown at the bottom in Figure 7. When the user selects a community from the list, theuser can view a graph using the SVG viewer, as shown in Figure 3. If the user judges
the community to be too large to understand easily, the threshold for dividing the communities can be adjusted by the user. If the threshold is increased, weak relationships

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Fig. 6. Local view for searching a particular researcher

will be eliminated, and as a result the user will obtain a screen similar to that shown in
Figure 7 for the threshold. In this way, the user can interactively use the system to findthe desired communities.

6 Conclusions
Community mining is a method to find communities with implicit knowledge. In thispaper, we propose a new approach to community mining, namely, including both amacro view of all researchers and a micro view of individual researchers. We implemented and demonstrated the proposed system. In the macro view, the systems showsclusters of networks as communities. Users can browse clusters of networks by controlling cluster size. In the micro view, users can focus on individual researchers and theirneighbors and can shift from one researcher to another. The system can be accessed on
the website listed in the following reference [12].
Although the proposed system is functional in its current state, a number of areas for
improvement remain. First, the proposed system must be tested by actual users under
actual conditions of use. We hope to receive feedback from researchers regarding oursystem and will make improvements based thereupon. Another area for improvement
is how to assist the user with the indexes. In this study, we do not use an automatic
approach for finding communities because of the variety of purpose for such a task. We
intend to investigate the support of a user driven community mining system, and also to468

R. Ichise, H. Takeda, and K. Ueyama

Fig. 7. List of communities

Numuber of communities

1000
th. = 1th. = 2th. = 3th. = 4th. = 5th. = 6th. = 7th. = 8th. = 9th. =10

100

10

1
1

10
Community size

100

Fig. 8. Number of communities

develop a computer driven community mining system. Finally, we must investigate theseamless integration of global and local views of communities.

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5. Garfield, E.: Citation indexes for science: A new dimension in documentation through association of ideas. Science 122 (1955) 108–111

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6. Motoda, H.: Active Mining. IOS Press (2002)
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