Self-Organized Service Management in Social Systems

Humans create efficient social structures in a self-organized way. People tend to join groups with other people with similar characteristics. This is call homophily. This paper proposes how homophily can be introduced in Service-Oriented Multiagent Systems to create efficient self-organized structures in which agents are linked to similar agents, where the similarity is based on the set of services that each agent provides and the roles they play. The results show that a greedy method can be used to locate services in the network and that homophily, which links similar services together, can produce a significant improvement in the performance of the search process. A second contribution is the study of the adaptation of the agents to the number and the type of services demanded. The paper shows how, considering just local information and making local decisions to stay or leave the system, the network adapts itself to a known service distribution.

[1]  Julita Vassileva,et al.  TRIBLER: a social‐based peer‐to‐peer system , 2008, IPTPS.

[2]  David D. Jensen,et al.  Navigating networks by using homophily and degree , 2008, Proceedings of the National Academy of Sciences.

[3]  Harry J. Holzer,et al.  Search Method Use by Unemployed Youth , 1986, Journal of Labor Economics.

[4]  Matthias Klusch,et al.  Automated semantic web service discovery with OWLS-MX , 2006, AAMAS '06.

[5]  S. N. Dorogovtsev,et al.  Evolution of networks , 2001, cond-mat/0106144.

[6]  Fernando Vega Redondo,et al.  Search and Homophily in Social Networks , 2010 .

[7]  Valeria De Antonellis,et al.  Service-Based Semantic Search in P2P Systems , 2009, 2009 Seventh IEEE European Conference on Web Services.

[8]  Victor R. Lesser,et al.  A Multi-Agent Approach for Peer-to-Peer Based Information Retrieval System , 2004, AAMAS.

[9]  David D. Jensen,et al.  Decentralized Search in Networks Using Homophily and Degree Disparity , 2005, IJCAI.

[10]  Xiaowei Xu,et al.  Investigating Homophily in Online Social Networks , 2010, 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.

[11]  Shi Xiao,et al.  On Degree-Based Decentralized Search in Complex Networks , 2006, ArXiv.

[12]  P. Lazarsfeld,et al.  Friendship as Social process: a substantive and methodological analysis , 1964 .

[13]  Shanshan Liu,et al.  Matching Algorithm of Web Services Based on Semantic Distance , 2009 .

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

[15]  Frances M. T. Brazier,et al.  Agents and Service-Oriented Computing for Autonomic Computing: A Research Agenda , 2009, IEEE Internet Computing.

[16]  Michael N. Huhns,et al.  Agents as Web Services , 2002, IEEE Internet Comput..

[17]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[18]  Les Gasser,et al.  Research Directions for Service-Oriented Multiagent Systems , 2005, IEEE Internet Comput..

[19]  Jon M. Kleinberg,et al.  The small-world phenomenon: an algorithmic perspective , 2000, STOC '00.

[20]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[21]  B. Weitz.  Hosted By , 2003 .

[22]  Lada A. Adamic,et al.  Zipf's law and the Internet , 2002, Glottometrics.

[23]  Alhussein A. Abouzeid,et al.  Modeling and analysis of random walk search algorithms in P2P networks , 2005, Second International Workshop on Hot Topics in Peer-to-Peer Systems.

[24]  Takahiro Kawamura,et al.  Semantic Matching of Web Services Capabilities , 2002, SEMWEB.