Small-world effects in lattice stochastic diffusion search

de Meyer, K., Bishop, J. M. and Nasuto, S. J. ORCID: https://orcid.org/0000-0001-9414-9049 (2002) Small-world effects in lattice stochastic diffusion search. In: Artificial Neural Networks - ICANN'02, 28-30 Aug 2002, Madrid, Spain, pp. 147-152.

Abstract/Summary

Stochastic 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.