Abstract.

Autonomy-Oriented Computing (AOC) is a bottom-up computing paradigm that is aimed (1) to tackle computationally hard problems (e.g., constrained optimization) and (2) to model complex systems behavior (e.g., emergent behavior).  In doing so, AOC emphasizes capturing the construct of synthetic autonomy in locally interacting agents, and utilizes the aggregated effects of agent interactions to generate desired global solutions or systems dynamics.  The fundamental working mechanism of self-organization that underlies AOC approaches offers the advantages of natural formulation as well as scalable performance in handling computational tasks or characterizing systems that are distributed and large-scale in nature (e.g., World Wide Web and social networks).

This talk will discuss how AOC approaches can be applied to unveiling the community structure as well as understanding the dynamic performance of social networks.  The social networks that we deal with may consist of socially interacting agents as involved in information exchange, experience sharing, and/or service transactions, based on certain local interaction policies.


References:

[1] Liu, J., Jin, X., and Tsui, K.C. (2005). Autonomy Oriented Computing (AOC): From Problem Solving to Complex Systems Modeling, Kluwer Academic Publishers / Springer (monograph).

[2] Liu, J. (2001). Autonomous Agents and Multi-Agent Systems: Explorations in Learning, Self-Organization and Adaptive Computation, World Scientific (monograph).

[3] Liu, J., Zhang, S., Yang, J. (2004). Characterizing Web usage regularities with information foraging agents, IEEE Transactions on Knowledge and Data Engineering, Vol. 16, No. 5, pp. 566–584.

[4] Yang, B., Liu, J., Cheung, W.K. (2007). Community mining from signed social networks, IEEE Transactions on Knowledge and Data Engineering (to appear).

[5] Zhang, S., Liu, J. (2006). From local behaviors to the dynamics in an agent network, Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006), Dec. 2006, pp. 572-580.