Finding Experts Using Social Network Analysis

2007 IEEE/WIC/ACM International Conference on Web Intelligence

Yupeng Fu, Rongjing Xiang, Yong Wang, Min Zhang, Shaoping Ma

Advisor: Dr. Koh Jia-LingReporter: Che-Wei, Liang

Date: 2009/03/231

Outline

• Introduction• A two-stage ranking method• Building associations among Candidates• Experiment• Conclusion

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Introduction

• When locating some desired information, – One can usually be satisfied by finding an expert in

the topic of interest

• Finding information quickly on the expertise of people quickly

– Can play critical roles in facilitating better solutions and fostering the formation of virtual organizations, expertise networks

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Introduction

• Discovering who knows what is challenging– Could build up a relationship between a query and

an expert via documents– Social networks provide another opportunity for

finding experts

• Email and web pages can be utilized to mine the relationship among people

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A two-stage ranking method

• Expert finding problem– Given the query topic q,

the probability of a candidate c being an expert

• To reveal the relationship between query & expert– How to associate the high semantic and abstract

concept “person” with concrete documents?• Two kinds of association to tackle this problem

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A two-stage ranking method

• First kind of association a(d, c)– Between the candidates and the content of the

documents

• Assume: – Each expert’s knowledge can be represented by

a list of terms– Document d where the candidate c appears has

non-zero associations a(d, c)

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A two-stage ranking method

• Second kind of association a(cx, cy)– Among the candidates themselves– The connection among candidates can be identified

through document analysis

• Given an expert cx , – the candidate cy who has strong association a(cx, cy)

with him is also quite likely to be an expert

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A two-stage ranking method

• Two-stage method 1. Expertise evaluating process– Identifying candidate c to be an expert

through sum over the similarities of all the documents for a given topic q

– Select some candidates ranking at top levels as seed

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A two-stage ranking method

2. Expertise propagation process– Employ the associations among candidates to

propagate the likelihood from those highly possible experts to other candidates

– Viewed as estimating the probability p(cy|cx),

the probability of candidate cy to be expert

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A two-stage ranking method

• If an expert cx has an expertise probability of P(cx) and w associated candidates, each of the w candidates cy has the association a(cx, cy) will receive a score fraction from cx

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Building associations among Candidates

• Task: Building associations• Represent the organization as a Graph– Nodes correspond to candidates, edges

correspond to the strength of associations– Higher strength of association indicates that the

two people have more common interest and more frequent communication

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Building associations among Candidates

• Web pages-based Social network– People co-occur in a range of local context may

share similar interest– Intuitive way, count the co-occurrence of two

candidates cx and cy in a document d

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Building associations among Candidates

• Email communication-based Social network– Two candidates cx and cy are associated if they

appear together in the from, to or cc field of an email e

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Building associations among Candidates

• Email communication-based Social network (cont.)

– Email connection matrix built merely through single message is sparse

– Consider associating candidates appearing in the same email thread

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Building associations among Candidates

• Email communication-based Social network (cont.)

– Combine single message and thread together

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Building associations among Candidates

• Query dependent social network– Calculate the associations from those web pages

and emails which are relevant to the query topic– Focus merely on the associations which are

related to the desired topic

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Building associations among Candidates

• Query dependent social network (cont.)

– Evaluate the strength of association by employing similarity of the documents to the query• The more relevant to query the document that joins

the candidates is, the stronger association exists among the candidates

– Replace the binary function with numerical function• Potential expert propagates more

expertise probability to those candidates with stronger associations

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Experiment

• Experimental settings– Text Retrieval Conference (TREC)• Provided a common platform with the Enterprise

Search Track to empirically assess methods• A crawl of the public W3C sites and comprises 331,037

documents in six different sub-collection including email lists web and personal homepages• 198,394 email messages totally form 79,521 email

trees

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Experiment

• Experimental results

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Experiment

• The role of seed

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Experiment

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Experiment

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Conclusion

• Propose a two-stage method for expert finding

• The performance improvement in experiments demonstrates its effectiveness

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