Expert Finding in a Social Network

Zhang, Jing; Tang, Jie; Li, Juanzi

doi:10.1007/978-3-540-71703-4_106

Cited by 159 publications

(87 citation statements)

References 4 publications

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“…published articles in certain topic) and then propagated possible expertise of a candidate according to their relationships (e.g. co-authored or supervised) [62].…”

Section: Associationmentioning

confidence: 99%

Investigating the Expertise Indicators of Vulnerability Discovery Professionals

Al-Banna¹,

Benatallah²,

Barukh³

2017

Adv. sci. technol. eng. syst. j.

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“…published articles in certain topic) and then propagated possible expertise of a candidate according to their relationships (e.g. co-authored or supervised) [62].…”

Section: Associationmentioning

confidence: 99%

Investigating the Expertise Indicators of Vulnerability Discovery Professionals

Al-Banna¹,

Benatallah²,

Barukh³

2017

Adv. sci. technol. eng. syst. j.

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“…Recently, social networks are also utilized to find professionals. Zhang et al [34] proposed an approach to find the expertise by considering both personal information and relationship information with other experts. Lappas et al [17] proposed to find a team of experts by considering the team formation problem in social networks.…”

Section: Related Workmentioning

confidence: 99%

Mechanism design for finding experts using locally constructed social referral web

Zhang

Huang

et al. 2012

2012 Proceedings IEEE INFOCOM

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Abstract-In this work, we address the problem of distributed expert finding using chains of social referrals and profile matching with only local information in online social networks. By assuming that users are selfish, rational, and have privately known cost of participating in the referrals, we design a novel truthful efficient mechanism in which an expert-finding query will be relayed by intermediate users. When receiving a referral request, a participant will locally choose among her neighbors some user to relay the request. In our mechanism, several closely coupled methods are carefully designed to improve the performance of distributed search, including, profile matching, social acquaintance prediction, score function for locally choosing relay neighbors, and budget estimation. We conduct extensive experiments on several datasets of online social networks. The extensive study of our mechanism shows that the success rate of our mechanism is about 90% in finding closely matched experts using only local search and limited budget, which significantly improves the previously best rate 20%. The overall cost of finding an expert by our truthful mechanism is about 20% of the untruthful methods, e.g.. the method that always selects high-degree neighbors. The median length of social referral chains is 6 using our localized search decision, which surprisingly matches the well-known small-world phenomenon of global social structures.

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“…One uses graph-based measures (such as HITS or PageRank) on social networks, extracted from email communications, to produce a ranking of experts [10,13]. Alternatively, others assume homogeneity among neighbours in a social network (based on co-authorship or organizational hierarchy) and define a smoothing procedure to relevance-based expert scores [17,32]. It is important to mention that in this paper we consider not only people, but also other types of entities (organizational units and geographical locations) as nodes.…”

Section: Related Workmentioning

confidence: 99%

A User-Oriented Model for Expert Finding

Смирнова

Balog

2011

Lecture Notes in Computer Science

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Abstract. Expert finding addresses the problem of retrieving a ranked list of people who are knowledgeable on a given topic. Several models have been proposed to solve this task, but so far these have focused solely on returning the most knowledgeable people as experts on a particular topic. In this paper we argue that in a real-world organizational setting the notion of the "best expert" also depends on the individual user and her needs. We propose a user-oriented approach that balances two factors that influence the user's choice: time to contact an expert, and the knowledge value gained after. We use the distance between the user and an expert in a social network to estimate contact time, and consider various social graphs, based on organizational hierarchy, geographical location, and collaboration, as well as the combination of these. Using a realistic test set, created from interactions of employees with a university-wide expert search engine, we demonstrate substantial improvements over a state-of-the-art baseline on all retrieval measures.

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Expert Finding in a Social Network

Cited by 159 publications

References 4 publications

Investigating the Expertise Indicators of Vulnerability Discovery Professionals

Investigating the Expertise Indicators of Vulnerability Discovery Professionals

Mechanism design for finding experts using locally constructed social referral web

A User-Oriented Model for Expert Finding

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