Structure Mining for Intellectual Networks
Ryutaro Ichise1 , Hideaki Takeda 1 , and Kosuke Ueyama 2
1

National Institute of Informatics,
2-1-2 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 is very important for researchers in order toundertake new research topics. In the present paper, we propose a community
mining system that helps to find communities of researchers using bibliography
data. The basic concept of the present study is to provide interactive visualizationof communities both local and global. We implemented this concept using actual
bibliography data and present a case study using the proposed system.

1 Introduction
Recently, communities have begun to include a large number of members because thishelps to make information more reliable. In such communities, only informative information can be propagated among several users. As a result, we can obtain usefuland reliable information from communities. This situation also exists in the field of research. Most researchers do not include all of the technology used in their experiments
when writing a paper because some techniques cannot be represented in words, suchas computer coding techniques. Such techniques propagating within the local researchcommunity may be useful for developing new technologies.
In order to clarify existing research communities, bibliography information is usedwidely. In co-citation analysis [12], all papers that are cited in a paper make up thecommunity of 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 [3] and Google
Scholar [6] 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 microcommunities, 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 researchers that has both local and global viewpoints. The proposed system
will facilitate understanding of the researcher community and will advance new areasof research.
The present paper is organized as follows. In Section 2, we discuss the proposed
design for a community mining system. In Section 3, we describe a community miningsystem implementation based on the design policies discussed in Section 2. In the Section 4, we explain the proposed system using a number of examples. Finally, in Section
5, we discuss our conclusions and areas for future study.

2 System Design
2.1 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 [11], citation [5] and co-citation [12]. In the present paper, we use three
relationships in the knowledge domain to find research communities:– co-authorship– citation– author citation
The first two relationships are well known among knowledge domain researchers.
With respect to co-authorship, if we can consider the researcher as a node and coauthorship as an arc, then we can obtain networks of researchers. We can consider that
researchers, whose nodes are linked to have the same research interests. With respect
to citation, if we can consider the research paper as a node and citations in papers as
arcs, then we can obtain networks of papers. The final relationship is author citation.
This relationship also represents relationships among authors. Research papers include
citations of papers, as described above. This citation indicates that both papers (whichhave a citation relationship) are related to the same subject. In the same manner, theauthors of both papers have the same research interests. In the present study, these
relationships are referred to as author citations and are used for community mining.
2.2 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 the active mining approach [9]. The basic concept of active mining is to utilize interactions
between the user and the computer. We separate mining method into two steps. The firststep is to automatically mine communities by computer, and the second step is to visualize the information in order to facilitate the understanding of the information by theuser. After viewing the communities that are discovered by computer, the user can specify a command to find more refined communities. As we described above, the proposed
method can facilitate the finding of communities through interactive manipulation of
the mining result obtained by computer.
Several types of indexes have been proposed for finding communities [4]. In thepresent study, we use indexes for finding communities as follows:
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. Therefore, thisindex may 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.

3 Research Community Mining System
3.1 System Architecture
In the present study, we implemented a community mining system using the policies
presented in the previous section. The components of the system are shown in Figure 1.
The system has two databases, the CiNii [2] database and an experiment database, andfive program units. The CiNii database will be described in detail later herein.
The database generation unit in Figure 1 selects records from the CiNii databaseand sends them to the database management unit. MySQL [10] is used as the databasemanagement unit. If possible, the mining index discussed in Section 2.2 is then calculated by the mining index calculation unit. The components are written in Perl. Notethat the entire mining index cannot be calculated at this stage because of the need for theuser query. The result of the calculated index is also stored in the experiment database,which is handled by the database management unit.
Users access the system through the visualization control unit, which is constructedusing a web browser that includes Flash Player and SVG Viewer. Then, when a user
inputs data from the web browser, the data will be sent to the I/O control unit via theInternet. The I/O control unit, which is constructed by a web server that includes the
CGI program, generates data for visualization from the user input. The components arealso 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.
3.2 Bibliography DatabaseIn the present study, we use the CiNii database to obtain bibliography information.
The database is described by approximately 320 megabytes of SGML data. The CiNii

Mining index
calculation unit

I/O control unit
Database
generation unit

Databasemanagement unit

CiNii
Database

Experimet
Dabatase

Visualization
control unit

Fig. 1. System architecture.

database contains bibliography entries, such as title, author and publication year. The
number of data are listed in Table 1. The number of database records in CiNii denotes
the number of records in the original database, as described in SGML format. The Paper and Researcher entries denote the number of records for papers and the number ofrecords for researchers, respectively. The number of researchers is less than the numberof 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 iscounted as three when three researchers write a paper corroboratively. The Co-author
entries denote the number of combinations of authors for a paper. For example, whena 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 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, 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 we prefer citing various papers to citing several
papers written by the same author. Note that the database used in the present study wascreated in October of 2003, and is therefore different from the current CiNii database.
3.3 Preprocessing of Database
In order to construct the experiment 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 some-Table 1. Number of data.
Number of records in CiNii Number of records in Experiment 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

times have multiple author names in single author field. For treating such records, thedatabase generation unit divides long author names using special characters, such as
. In addition, a number of other small record linkage techniques are also used in thisstage.
After the preprocessing has been completed, the experiment database is created by
the database management unit. The numbers of records for the experiment database arelisted in Table 1. The experiment database contains much less data than the originalCiNii database because the bibliography of the paper included in the CiNii databaseas original information was only used. Although the CiNii database contains cited paper information, the information is not complete and contains several mistakes, and istherefore not used in the present experiment.
3.4 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 the datainteractively. For this purpose, we used a web browser, which includes Flash Player
and SVG Viewer, as a visualization control unit. Data for this unit is provided by a
web server on the Internet. The server is referred to as the I/O control unit. We used
two types of visualization data provided by the I/O control unit: global visualization by
SVG [13] and local visualization by Flash. Global visualization by SVG facilitates thelocation of 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 inthis section.
We first discuss graph visualization by SVG. As discussed in Section 2.1, representing the relationships between communities in the form of a graph will facilitate
the finding of communities. Therefore, we use a graphic representation scheme visualization by SVG. SVG is an XML format for representing graphs. The SVG file canbe visualized by an SVG viewer. An example of SVG graph visualization of the relationships among researchers is shown in Figure 2. However, using such a graphic
representation is not suitable for finding communities in the graph, because most of thenodes will be connected [1]. As a result, the graph could be very large and the usermight not be able to find communities in the graph. In order to discern communities
from a large graph, the system should have a function that shows parts of the graph that

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Fig. 2. An example of graph representation by SVG.

may contain the desired communities. Therefore, we herein propose interactive mining
in conjunction with the indexes presented in Section 2.2. When the user specifies anindex and its threshold, the system automatically divides the graph. This is very important for finding communities because it is hard to specify an index to match the purposeof the user. 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 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 to visualize 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,
as discussed in Section 2.1. An example of the local point of view is shown in Figure 3. The circle in the center represents the currently specified researcher of focus. 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.

Fig. 3. An example of a local view.

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.
3.5 Other Functions
In addition, we implement basic functions in order to facilitate the finding of communities by the user as follows:– Paper search: The user can search a paper or researcher by specifying author nameor 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.

4 Case Study of Community Mining System
In order to demonstrate how the proposed system works, we will discuss the systemusing actual examples. Figure 4 illustrates the starting screen of the proposed system.
From this screen, we can easily choose any of the functions of the proposed system.
From the center of the starting screen, we can access the local view by Flash, as
discussed in Figure 3. By clicking the center of the starting screen, a search window is
opened. After the user inputs the name of a researcher, whom the user wants to know

Fig. 4. Starting screen of system.

more about, a visualized screen representing the local communities for the researcher
is shown. For example, if we input Takeda (in Japanese) and choose Hideaki Takeda
from the Takeda list, the screen shown in Figure 5 will be displayed. The list at thebottom-left corner of the screen is a list of authors. When Hideaki Takeda is selectedfrom this list, a circle denoting Hideaki Takeda is shown at the center of the screen. The
circles around Takeda denote researches who have co-authored papers with Takeda.
In order to find communities that are built around a specified researcher, the user isable visualize the researchers related to a specified researcher. In addition, the system
has a function for eliminating relationships that do not exceed a particular thresholdthat is specified by user, as well as a function for changing the scale or location of
circles in order to more clearly display the desired information. Although Figure 5
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 thebibliography of the researcher at the center of the screen and is used to determine thefield of the researcher.
The two items in the box at bottom-left, shown in Figure 4, 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 bottom-right, shown in Figure 4, 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 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, as shown at the center-left of the screen, the weight of therelationships 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Fig. 5. Local view for searching a particular researcher.

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 constructedwith 26 researchers. The list of the communities is shown at the bottom in Figure 6.
When the user selects a community from the list, the user can view a graph using SVG
viewer, as shown in Figure 2. 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 will be eliminated, and as a result theuser will obtain a screen similar to that shown in Figure 6 for the threshold. In this way,
the user can interactively use this system to find the desired communities.

5 Conclusions
In the present paper, we propose a novel system for community mining, which requires
both a macro view of all researchers and a micro view of individual researchers. We implemented and demonstrated the proposed system, which can be accessed at the websiteof the following reference [7].
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 the present 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 todevelop a computer driven community mining system. Finally, we must investigate theseamless integration of global and local views of communities.

Fig. 6. List of communities.

References
1. Barabási, A-L.: LINKED: The New Science of Networks, 2002.
2. National Institute of Informatics, CiNii (Citation Information by NII), http://ci.nii.ac.jp/,
2004.
3. Scientific Literature Digital Library, http://citeseer.ist.psu.edu/, 2004.
4. Freeman, L. C.: Centrality in social networks: Conceptual clarification, Social Networks,
Vol. 1, pp. 215-239, 1979.
5. E. Garfield: Citation Indexes for Science, Science, Vol. 122, No. 3159, pp.108-111, 1955.
6. Google Scholar, http://scholar.google.com/, 2004.
7. R. Ichise and H. Takeda: Community Mining System, http://irweb.ex.nii.ac.jp/, 2005.
8. M. Kessler: Bibliographic Coupling between Scientific Papers, American Documentation,
Vol. 14, No. 1, pp. 10-25, 1963.
9. H. Motoda: Active Mining, IOS Press, 2002.
10. MySQL, http://www.mysql.com/, 2004.
11. M. E. J. Newman: Coauthorship Networks and Patterns of Scientific Collaboration, Proceedings of the National Academy of Sciences of the USA, Vol. 101, suppl. 1, pp. 5200-5205,
2004.
12. H. Small: Co-citation in the Scientific Literature, Journal of the American Society of Information Science, Vol. 24, pp. 265-269, 1973.
13. Scalable Vector Graphics (SVG) 1.1 Specification, http://www.w3.org/TR/SVG/, 2003.