We next describe the algorithm used to calculate authoritativeness of a node. A related but differ- ent concept is the trust value of a node: The trust calculation in social networks has been addressed in several studies. The EigenTrust algorithm is used in peer-to-peer systems and calculates trust using a variation on the PageRank algorithm (Kamvar, Schlosser, & Garcia-Molina, 2003)
Name Activation Freq. Comment10
1 Toyoaki Nishida 5.53 624 Former Commissioner of JSAI, Prof. 2 Toru Ishida 4.98 574 Former Commissioner of JSAI, Prof. 3 Hideyuki Nakashima 4.52 278 Former Commissioner of JSAI
4 Koiti Hasida 4.50 345 Commissioner of JSAI
5 Mitsuru Ishizuka 4.24 377 Commissioner of JSAI, Prof. 6 Hiroshi Okuno 3.89 242 Commissioner of JSAI, Prof. 7 Riichiro Mizoguchi 3.60 404 Commissioner of JSAI, Prof.
8 Seiji Yamada 3.35 168 Associate Prof.
9 Hideaki Takeda 3.22 435 Associate Prof. 10 Takahira Yamaguchi 3.10 236 Prof. 11 Yukio Ohsawa 2.98 185 Associate Prof.
12 Hozumi Tanaka 2.90 465 Chairperson of JSAI, Prof. 13 Takenobu Tokunaga 2.89 302 Associate Prof.
14 Koichi Furukawa 2.77 141 Former Commissioner of JSAI, Prof.
15 Tatsuya Kawahara 2.74 440 Prof.
Name Activation Freq Comment
1 Yutaka Matsuo 230.6 136 Himself
2 Mitsuru Ishizuka 28.7 377 His former supervisor, co-author 3 Yukio Ohsawa 19.5 185 His former project leader, co-author 4 Toyoaki Nishida 14.5 624 Professor of lecture at university 5 Naohiro Matumura 13.5 82 My former colleague, co-author
6 Seiji Yamada 12.7 168 Acquaintance
7 Takafumi Takama 12.3 16 Former researcher of my former laboratory
8 Toru Ishida 12.1 574 A member of the advisory board of his research center 9 Takahira Yamaguchi 11.5 236 Acquaintance
10 Hidehiko Tanaka 11.3 842 University professor Table 6. Result of authority propagation
for a peer-to-peer system. Richardson, Agrawal, and Domingos (2003) use social networks with trust to calculate the belief a user might have in a statement. Golbeck and Hendler (2005) proposed algorithms for inferring trust relationships among individuals that are not directly connected in the network. These approaches find paths from the source to any node, concatenating trust values along the paths to reveal, eventually, the recom- mended belief or trust for the path.
Once algorithms for calculating trust have been designed properly, the next step is to use them for applications. With the current large amount of social network data available from the Web, several studies have addressed integrating network analysis and trust into applications. One promising application of trust is a recommendation system. Golbeck and Hendler (2004) developed TrustMail, an e-mail client that uses variations on these algorithms to score email messages in the user’s inbox based on the user’s participation and ratings in a FOAF network. They developed the Trust Module for FOAF, which extends the FOAF vocabulary by adding a property by which users state how much they trust one another. FilmTrust, which integrates social network and movie reviews, is another trust-based system (Golbeck & Parsia, 2006). System users can rate films and write reviews. In addition, they maintain a set of friends who are rated according to how much the user trusts their opinion of movies. In both systems, trust took on the role of a recom- mendation system forming the core of algorithms to create predictive rating recommendations for emails and movies.
In addition to recommendation systems, an- other application in which trust takes on an impor- tant role is information sharing. With the current development of tools and sites that enable users to create Web contents, users can easily disseminate various kinds of information. In social network- ing services (SNSs), a user creates a variety of contents including public and private information. Although these tools and sites enable users to
easily disseminate information on the Web, users sometimes have difficulty sharing information with the right people and frequently have privacy concerns because large amounts of information including private photos, diaries, and research notes in the SNSs are neither completely open nor closed. One approach to tackle the information sharing issue on SNSs is to use a trust network. Availability information in the real world is often closely guarded and shared only with the people in one’s trust relationships: Confidential project documents which are limited to share within a division of company might be granted access to data of another colleague who is concerned with the project. By analogy with the examples in the real world, we find that social trust relationships are applicable to the process of disseminating and receiving information on SNSs.
Several studies have addressed integration of social networks and trust into information shar- ing. Goecks and Mynatt (2004) propose a Saori infrastructure, which uses social networks for information sharing. In their system, access to post and edit Website information is controlled by relationships among users. Mori et al. (2005) propose a real-world oriented information shar- ing system using social networks, which enables users to control the information dissemination process within social networks. The system enables users to analyze their social networks so that they can decide who will have rights to access their information. For example, if a user wants to diffuse information, he might consider granting access to a person who has both high degree and betweenness on his network. On the other hand, he must be aware of betweenness when the information is private or confidential. The users can also control information access using trust. For example, a user can give information access rights to other users who have certain trust relationships. The user can directly assign trust in a numerical value to a person in his relation. Then, trust can automatically be inferred using several algorithms, as mentioned earlier.
Other applications include navigation using a social network. Polyphonet is used for academic conferences to navigate researchers: If one would like to know someone, that person can see the information of the other person, even the loca- tion information at the conference site is avail- able. Flink, developed by Mika, also navigates researchers of semantic Web. It is a very useful site if we wish to know a researcher, and associ- ated collaborators.
Jin et al. (2006) uses the social network of contemporary arts as a navigation site for the International Triennale of Contemporary Art
(Yokohama Triennale 2005). At exhibitions, it is usual that participants enjoy and evaluate each work separately. However, our presupposition was that if participants knew the background and relations of the artists, they might enjoy the event more. For that purpose, the system provided rela- tions of artists and the evidential web pages for users. The system interface is shown in Figure 16. It was implemented using Flash display software to realize interactive navigation. The system pro- vides a retrieval function. The information about the artist is shown on the left side if a user clicks a node. In addition, the edges from the nodes are
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highlighted in the right-side network. The user can proceed to view the neighboring artists’ informa- tion sequentially, and can also jump to the web pages that show evidence of the relation.
The recent interesting application of extracted social network online is detecting conflict of in- terests (COI) of researchers (Aleman-Meza et al., 2006). Using the network extracted from FOAF data and DBLP (Computer Science Bibliography), potential COI are suggested. Although the rules are constructed heuristically, the results show the usefulness of the social networks in actual use for assigning reviewers of papers and proposals.