Small-world effects in lattice stochastic diffusion searchde Meyer, K., Bishop, J. M. and Nasuto, S. J. (2002) Small-world effects in lattice stochastic diffusion search. In: Artificial Neural Networks - ICANN'02, 28-30 Aug 2002, Madrid, Spain, pp. 147-152. Full text not archived in this repository. It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. Official URL: http://dl.acm.org/citation.cfm?id=684457&CFID=3760... Abstract/SummaryStochastic Diffusion Search is an efficient probabilistic bestfit search technique, capable of transformation invariant pattern matching. Although inherently parallel in operation it is difficult to implement efficiently in hardware as it requires full inter-agent connectivity. This paper describes a lattice implementation, which, while qualitatively retaining the properties of the original algorithm, restricts connectivity, enabling simpler implementation on parallel hardware. Diffusion times are examined for different network topologies, ranging from ordered lattices, over small-world networks to random graphs.
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