Finding Social Network for Trust Calculation

Yutaka Matsuo, Hironori Tomobe, Koiti Hasida and Mitsuru Ishizuka

National Institute of Advance Industrial Science and Technology (AIST)Jemail: y.matsuo@carc.aist.go.jp

University of Nagoya, Japan email: tomobe@nagao.nuie.nagoya-u.ac.jpAIST, Japan email: hasida.k@aist.go.jp

University of Tokyo, Japan ishizuka@miv.t.u-tokyo.ac.jp

ECAI 2004

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Outline

• Abstract• Introduction• Social Network Extraction

• Invention of Nodes and Edges

• Extraction of Edge Label

• Example and Evaluation

• Trust Calculation• Social Trust

• Individual Trust

• Related Works and Conclusion

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Abstract

• Trust is a necessary concept to realize the Semantic Web.

• But how can we build a “Web of Trust”?• Small “Web of Trust” => A huge “Web of Trust.”

• Focus on an academic community :• as a “microcosm” of a “Web of Trust” • to generate a social network automatically.

• Each edge is given a label • Coauthor , Lab , Proj , Conf .

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Introduction

• Based on the trust network, the computer can decide how trustworthy persons, resources, and pieces of information are.

• At the beginning : • A person or an organization will trust some acquaintances. • A trust network appears locally and grows gradually by adding

new nodes and edges.

• According to social scientists : • A person can name 200 to 5000 people• Relations are dynamic • New relations appear every day and old relations weaken

gradually.

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Introduction• Aspects of Knowledge Transfer

CurrentStudy

Structuralstrong vs. weak ties

Relationaltrust

Knowledgetacit vs. explicit

Hansen, 1999

Tsai & Ghoshal, 1998

Mayer et al., 1995

Zand, 1972

Zaheer et al., 1998

Nonaka, 1994

Polanyi, 1966

Zander & Kogut, 1995

Szulanski,1996

Krackhardt, 1992

Ghoshal et al., 1994

Granovetter, 1973

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Introduction

• Berners-Lee : Layer Cake• metadata , ontologies, rules, proofs,

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Social Network Extraction

• An academic society retains member profiles • name, affiliation, qualification,contact address …

• Rregular annual conference:• JSAI99, JSAI2000, JSAI2001, and JSAI2002• 1500 people• Choose 150 members to illustrate network

• Edge label :• Coauthor: Coauthors of a technical paper• Lab: Members of the same laboratory or research institute• Proj: Members of the same project or committee• Conf: Participants of the same conference or workshop

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Social Network Extraction

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Social Network Extraction

• For example• ‘Yutaka Matsuo” (denoted X)

• “Hironori Tomobe” (denoted Y)

• query “X and Y” to get a documents

• query “X or Y” to get b documents

• “X and (A or B or . . .)” .. “Y and (A or B or . . .)”

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Social Network Extraction

• Edge Label:• Retrieved by the query “X and Y” and get 3 pages.

• First checked 275 pages manually and assigned labels to each page.

• manually-selected word groups to characterize pages

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Social Network Extraction

• C4.5

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Social Network Extraction

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Trust Calculation• PageRank-like model to measure authoritativeness of each

member. • v : member number v = 1509

• n : iterations number set n=1000

• Neighbor(v) : set of nodes each of which is connected to node v

• c : constant for normalization

• E(v) : uniform over all nodes

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Trust Calculation

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Trust Calculation• Individual Trust

• n=300 , Vtarget = Yutaka Matsuo

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Relate Works and Conclusion

• First extract a list of members in the community, and try to determine their social network.

• Used the contents of the retrieved documents to classify the relation into four categories.

• Dan Brickley and Libby Miller invented an RDF vocabulary called FOAF (Friend-of-a-Friend) to create a social network.

• In this paper, we argue how local trust networks will finally constitute a huge “Web of Trust.”