Searching for expertise in social networks: a simulation of potential strategies

People search for people with suitable expertise all of the time in their social networks - to answer questions or provide help. Recently, efforts have been made to augment this searching. However, relatively little is known about the social characteristics of various algorithms that might be useful. In this paper, we examine three families of searching strategies that we believe may be useful in expertise location. We do so through a simulation, based on the Enron email data set. (We would be unable to suitably experiment in a real organization, thus our need for a simulation.) Our emphasis is not on graph theoretical concerns, but on the social characteristics involved. The goal is to understand the tradeoffs involved in the design of social network based searching engines.

[1]  Lynn A. Streeter,et al.  Who Knows: A System Based on Automatic Representation of Semantic Structure , 1988, RIAO Conference.

[2]  Volker Wulf,et al.  Sharing Expertise: Beyond Knowledge Management , 2002 .

[3]  D. Wegner,et al.  Transactive memory in close relationships. , 1991, Journal of personality and social psychology.

[4]  Munindar P. Singh,et al.  Searching social networks , 2003, AAMAS '03.

[5]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[6]  P. Killworth,et al.  The reversal small-world experiment , 1978 .

[7]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[8]  D. Watts,et al.  An Experimental Study of Search in Global Social Networks , 2003, Science.

[9]  L. N. Foner,et al.  A multi-agent, referral based matchmaking system , 1997 .

[10]  Mark S. Ackerman,et al.  Expertise recommender: a flexible recommendation system and architecture , 2000, CSCW '00.

[11]  H. Russell Bernard,et al.  Index: An Informant-Defined Experiment in Social Structure , 1982 .

[12]  Lada A. Adamic,et al.  How to search a social network , 2005, Soc. Networks.

[13]  Mark S. Granovetter.  The Strength of Weak Ties , 1973, American Journal of Sociology.

[14]  Lada A. Adamic,et al.  Search in Power-Law Networks , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  Bart Selman,et al.  The Hidden Web , 1997, AI Mag..

[16]  Robert Axelrod.  Advancing the art of simulation in the social sciences , 1997 .

[17]  R. Burt.  The Network Structure Of Social Capital , 2000 .

[18]  Munindar P. Singh,et al.  An adaptive social network for information access: Theoretical and experimental results , 2003, Appl. Artif. Intell..

[19]  Bonnie A. Nardi,et al.  It's Not What You Know, It's Who You Know: Work in the Information Age , 2000, First Monday.

[20]  Howard L. Kingsley.  An Experimental Study of 'Search' , 1932 .

[21]  Loren G. Terveen,et al.  Contact management: identifying contacts to support long-term communication , 2002, CSCW '02.

[22]  Sharon L. Milgram,et al.  The Small World Problem , 1967 .

[23]  M E J Newman,et al.  Identity and Search in Social Networks , 2002, Science.

[24]  Hector Garcia-Molina,et al.  Improving search in peer-to-peer networks , 2002, Proceedings 22nd International Conference on Distributed Computing Systems.

[25]  Mark E. J. Newman.  A measure of betweenness centrality based on random walks , 2005, Soc. Networks.

[26]  Leonard N. Foner,et al.  Yenta: a multi-agent, referral-based matchmaking system , 1997, AGENTS '97.

[27]  E. Hutchins.  Cognition in the wild , 1995 .

[28]  Richard W. Hamming,et al.  Error detecting and error correcting codes , 1950 .