Community Mining Tool using Bibliography Data
Ryutaro Ichise, Hideaki Takeda
National Institute of Informatics
2-1-2 Hitotsubashi Chiyoda-ku
Tokyo, 101-8430, Japan{ichise,takeda}@nii.ac.jp
Abstract
Research communities are very important for researchers undertaking new research topics. In this paper,
we propose a community mining system using bibliography
data in order to find communities of researchers. The basic
concept of our study is to provide interactive visualization ofboth local and global research communities. We implement
this concept using actual bibliography data and present acase study using the proposed system.

Kosuke Ueyama
TRIAX Inc.
4-18-2-203 Takadanobaba, Shinjyuku-ku
Tokyo, 169-0075, Japan
ko@triax.jp

nity mining system for researchers. This mining system has
both local and global viewpoints. The proposed system will
facilitate understanding of researcher communities and will
advance new areas of research.
The present paper is organized as follows. In Section 2,
we discuss the proposed design for the community mining
system. In Section 3, we describe and analyze the bibliography data used in our system. In Section 4, we describe
our visualization technologies. In Section 5, we explain the
proposed system using a number of examples. Finally, in
Section 6, we discuss our conclusions and areas for future
study.

1 Introduction
2 System Design
As information technologies progress, we can obtain research information faster then before. However, technologies covering a wide area can change just as rapidly. Therefore, all researchers not only must continuously follow new
trends of research but also investigate new research topics.
When we undertake new research topics, we need to know
the research communities of researchers with the same research topic or same interest. As a result, we need an effective community mining tool for finding them.
In order to identify existing research communities, bibliography information is widely used. In co-citation analysis [12], all papers that are cited in a paper make up thecommunity of a certain research area. However, this paperbased analysis overlooks the individual researchers. As
a result, realizing that each researcher has an individual
research area is difficult. In contrast, CiteSeer [11] andGoogle Scholar [4] are able to handle research communities from a micro viewpoint because they handle co-author
and citation information from bibliographies and use the information for individual researchers. Although these systems are good for finding local communities involving an
author, they are not suitable for finding research communities close to the author. Moreover, it is not suitable forfinding the position of the author within a global researchcommunity. In the present paper, we propose a commu-

2.1 Relationships for Communities
The most important information for finding communities
of researchers is contained in a bibliography, which constitutes a paper list of the researchers and citation information of the papers in the list. In the networks of research,several relationships can be obtained from this information. The relationships in the knowledge domain include
co-authorship [10], bibliographic coupling [6], citation [3]and co-citation [12]. In this paper, we use the following
three relationships in the knowledge domain to find researchcommunities: 1) co-authorship, 2) citation, 3) author citation.
The first two relationships are well known among knowledge domain researchers. With respect to co-authorship, if
we consider the researcher as a node and the co-authorshipas an arc, we can obtain networks of researchers. We canthen consider the researchers with linked nodes to have thesame research interests. With respect to citation, if we consider the research paper as a node and the citations in papers
as arcs, we can obtain networks of papers. We can then consider the papers with linked nodes to have the same topic.
The final relationship is author citation. This relationshipalso represents relationships among authors. As we briefly

2.3 System Architecture

mentioned above, we assume that a citation indicates that
papers are related to the same subject. In the same manner,
we can consider that the authors of papers (which have a citation relationship) have the same research interests. In thepresent study, these relationships are referred to as author
citations.

In the present study, we implemented a community mining system using the concepts presented in the previous section. The components of the system are shown in Figure 1.
The system has two databases and five program units. The
two databases are the CiNii(Citation Information by the National Institute of Informatics) [9] database and an experimental database. The CiNii database will be described indetail later.
The five program units are shown in Figure 1. The
database generation unit selects records from the CiNii
database and sends them to the database management unit.
MySQL [8] is used as the database management unit. If
possible, a mining index discussed in Section 2.2 is thencalculated by the mining index calculation unit. The components are written in Perl. Note that the entire mining index cannot be calculated at this stage because of the needfor the user query. The result of the calculated index is also
stored in the experimental database, which is handled by the
database management unit.
Users access the system through the visualization control
unit, which is constructed using a web browser that includes
Flash Player and SVG (Scalable Vector Graphics) Viewer.
Then, when a user inputs data from the web browser, thedata is sent to the I/O control unit via the Internet. The I/O
control unit, which is constructed by a web server that has a
CGI(Common Gateway Interface) program, generates datafor visualization from the user input. The components arealso written in Perl. The I/O control unit calls the databasemanagement unit to retrieve data and calls the mining index
calculation unit to calculate the mining index.

2.2 Community Mining

In this section, we describe the community mining
method for the networks of research. We use a policy forcommunity mining borrowed from the active mining approach [7]. The basic concept of active mining is to utilize
spiral interactions between the user and the computer. We
separate the mining method into two steps. The first step
automatically mines communities by computer, and the second step visualizes the information in order to facilitate theuser’s understanding of the information. After the communities are discovered by computer in the first step, the user
can see the result in the second step. If the user is not satisfied with the communities discovered, the user can thenspecify parameters to further refine the communities. Then,the first step is conducted again with the new settings todiscover more communities. As we described above, theproposed method can facilitate the finding of communities
through spiral manipulation of the mining results obtainedby computer.
Several types of indexes have been proposed for finding
communities [2]. In the present study, we use the following
three indexes for finding communities: 1) simple weight, 2)
maximum flow, 3) closeness. The first index, simple weight,
is a measure for weighting arcs: for example, a weight canbe applied to represent the number of co-authors. When
the weight is small, the arc is considered to represent an
unimportant relationship. Therefore, highly weighted arcs
represent communities, and thus can be used as an index for
a community. The second index, max flow, is a measurethat focuses on the connection between two different nodes.
This index considers the weight on an arc as the communication capacity and measures the connection between thetwo nodes. The index is able to calculate the distance between two nodes that are not directly connected. When thetwo nodes have a thick connection, even though they haverelay nodes, the index has a high value. Therefore, this index may be a good measure for finding communities. The
third index, closeness, is a measure used to calculate the distance between two nodes. This index represents the shortest
distance between the two nodes. Therefore, when the distance is small, the two nodes are considered to be in thesame community.

3 Data extraction and analysis
3.1 Bibliography DatabaseIn the present study, we use the CiNii database to obtain bibliography information. The database is composedof approximately 320 megabytes of SGML(Standard Generalized Markup Language) data. The CiNii database contains bibliography entries, such as title, author and publication year. The number of data are listed in Table 1. The
entries in the CiNii column denote the number of records
in the original database. The Paper and Researcher entries
denote the number of records for papers and the numberof records for researchers, respectively. The number of researchers is less than the number of papers, because one
researcher could write more than one paper. The Author
entries in Table 1 denote the number of authors for each paper. For example, the record is three when three researchers
write a paper corroboratively. The Co-author entries denote2

Mining index
calculation unit

I/O control unit
Database
generation unit

CiNii
Database

Visualization
control unit

Databasemanagement unit

Experimental
Dabatase

Figure 1. System architecture.

3.2 Preprocessing of the Database

Table 1. Number of data records.

Paper
Researcher
AuthorCo-author
Citation (Paper)
Citation (Author)

CiNii(×1, 000)
544
224
787
1103
445
1562

Experimental Database(×1, 000)
128
90
358
231
36
349

In order to construct the experimental database, the CiNii
data was preprocessed by the database generation unit.
First, the database generation unit selected the useful attributes from the CiNii database; most of the attributes, suchas the ISSN (International Standard Serial Number), were
not relevant to this experiment. In addition, the databasegeneration unit conducted record linkage for the author
records. It has heuristics for dividing author records, because the author records in the CiNii database sometimes
contain multiple author names in a single author field. For
treating such records, the database generation unit divides
long author names using special characters, such as . Also,a number of other small record linkage techniques wereused in this stage.
After the preprocessing was completed, the experimental database was created from the CiNii database by the
database management unit. The resulting number of records
for the experimental database are listed in Table 1. The experimental database contains much less data than the original CiNii database because only the original bibliographiesof papers were used. Although the CiNii database contains
cited paper information, the information is not complete andcontains several mistakes, and was therefore not used in theexperimental database.

the 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 number of other papers, the Citation (Paper)
entries are lower than the Paper entries because the CiNii
database does not include citation information for all papers. The Citation (Author) entries denote the number of
researchers who wrote cited papers, with duplication. In a
comparison of the Paper and Researcher entries, the number of researchers is less than the number of papers. In contrast, in a comparison of the Citation (Paper) and Citation(Author) entries, the number of Citation (Author) entries islarger than the number of Citation (Paper) entries, which
implies that researchers prefer citing papers by a variety ofauthors to citing several papers written by the same author.
Note that the database used in the present study was created in October of 2003, and is therefore different from thecurrent CiNii database.

4 Visualization
The visualization control unit creates a visualization
screen for finding communities easily. In order for a user
to find a community, the user must be able to browse thedata interactively. For this purpose, we used a web browser,
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tion that shows the parts of the graph that may contain thedesired communities. Therefore, we herein propose interactive mining in conjunction with the indexes presented in
Section 2.2. When the user specifies an index and its threshold, the system automatically divides the graph. This is very
important for finding communities because it is hard for the
I/O unit to specify an index to match the intentions of theuser. Although the current implementation of the mining
index calculation unit calculates the three indexes presented
in Section 2.2, the I/O control unit can only create an SVG
graph for a simple weight.

Figure 2. An example of a graph representation by SVG.

which includes Flash Player and SVG Viewer, as the visualization control unit. The data for this unit is provided bya web server on the Internet. The server is referred to as the
I/O control unit. We used two types of visualization data:
global visualization by SVG [13] and local visualization byFlash. Global visualization by SVG facilitates the locationof global communities by showing global relationships on
a graph, whereas local visualization by Flash facilitates thelocation of local communities by showing the local relationships near the individual. Both of these methods are
discussed below.

4.2 Local Visualization
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 the communities. We propose visualizing the local communities towhich a specified researcher belongs in order to facilitate
the refinement of communities globally. Locally, a specified
researcher is located at the center of a field, and related researchers are placed around the circumference of the field.
This visualization is suitable for finding communities thatare built around a researcher. Since we focused on communities of researchers, the current implementation includes
only the relationships of the co-author and author citations,as discussed in Section 2.1. An example of the local pointof view is shown in Figure 3. The circle in the center represents the initially specified researcher. The surrounding
circles represent researchers who are related to the specified researcher. The outermost ring of circles represents
researchers who are related to the researchers of the innerring. The size of each circle represents the number of pa-

4.1 Global Visualization
We first discuss the graph visualization by SVG. As
discussed in Section 2.1, representing the relationships
between communities in the form of a graph facilitates
the finding of communities. SVG is an XML(eXtensible
Markup Language) format for representing graphs. The
SVG file can be visualized by an SVG viewer. An example
of a SVG graph visualization of the relationships among
researchers is shown in Figure 2. However, using such agraphic representation is not suitable for finding communities, because most of the nodes would be connected [1]. As
a result, the graph could be very large and the user might
not be able to find the communities. In order to discerncommunities from a large graph, the system needs a func4

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Figure 5. Local view for searching a particular researcher.

proposed system.
From the center of the starting screen, we can access the
local view by Flash, as shown in Figure 3. By clicking thecenter of the starting screen, a window is opened. Let us
explain by an example, shown in Figure 5. First, the user
inputs the name of a researcher in the search window (1).
In this example, we input Takeda (in Japanese). Then, alist of authors named Takeda is shown in the author list (2).
When Hideaki Takeda is selected from this list, a circle denoting Hideaki Takeda is shown at the center of the screen.
The circles around Takeda denote researchers who have coauthored papers with Takeda. In order to find communities
that are built around this specified researcher, the user canview related researchers. In addition, the system has a function for eliminating relationships that do not meet a userspecified threshold. The user selects the threshold using theslide bar (3). Then, to more clearly display the resulting
information, the user can change the scale or location of
circles by using the tool box (4) . In addition to the coauthor relationships shown in Figure 5, the proposed system
can also show the author citation relationships in different
colors by specifying the graph type (5). The bottom-rightwindow (6) shows the bibliography of the researcher at thecenter of the screen and is used to determine the field of theresearcher.

Figure 4. Starting screen of the proposed system.

pers that were written by the researcher. The thickness of
the line represents the strength of the relationships.

5 Case Study of the Community Mining System

The two items in the box at the bottom-left in Figure 4
are used for searching authors and papers. The two itemsin the center are used to show rankings. These rankings
include researchers with whom papers have been written,
and papers that are cited frequently. This function is used
for obtaining additional knowledge for community mining.

In order to demonstrate how the proposed system works,
we will discuss the system using actual examples. Figure 4
illustrates the starting screen of the proposed system. From
this screen, we can easily choose any of the functions of the5easy understanding of the global and local relationships between researchers. Therefore, we think our system can beused not only as a community mining system but also as
a new analytical tool for research. On the other hand, therelationships we used in this paper have several meanings,
because the links not only represent the same interests or
topics, but also the references for broad areas of research.
This produces complicated graphs in some cases. Therefore, in future research, we plan to analyze our assumptions
about community mining relationships.
Also, 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 conditions of actual use. We hope to receive feedback from researchers regarding our system and will make
improvements based thereupon. Another area for improvement is how to assist the user with the indexes. In thepresent study, we do not use an automatic approach for finding communities because of the options for such a task. We
intend to investigate the support of a user-driven community mining system, but also to develop a computer-driven
community mining system. Finally, we must investigate theseamless integration of the global and local views of communities.

Figure 6. List of communities.

The three items in the box at the bottom-right in Figure 4
are used for visualizing the graphs for community mining.
These items show citation relationships, author citation relationships, and co-author relationships. Since each of thesefunctions is similar, we will show only an author citation relationship. When the user clicks the area for the author citation in Figure 4, the screen shown in Figure 6 is displayed.
From this screen, the threshold of the weight of the relationships can be set. In this example, the weight of the relationships is set to 1, as shown at the center-left of the screen.
In other words, the graph is created using relationships inwhich the two researchers have at least one author citation
relationship. As a result, we can divide the researchers into
a number of communities. For example, we can obtain acommunity constructed with 30,536 researchers or a community constructed with 26 researchers. The list of communities is shown at the bottom in Figure 6. The user can select a community from the list and then view a graph using
the SVG viewer, as shown in Figure 2. If the community
appears too large to understand easily, the user can adjust
the threshold for dividing the communities. If the thresholdis increased, weak relationships will be eliminated, and theuser would obtain a screen similar to that shown in Figure 6.
In this way, the user can interactively use this system to findthe desired communities.

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6 Conclusions
In this paper, we propose a novel system for community mining. Our system presents both a global view of all
researchers and a local view of individual researchers. We
implemented and demonstrated the proposed system, which
can be accessed at the web site stated in reference [5]. From
our case study, we can conclude that the system provides
6