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RANDOM GRAPHS IN A NEURAL COMPUTATION MODEL 

Author: Alexandros Gerbessiotis a
Affiliation:   a Computer Science Department, New Jersey Institute of Technology, Newark, NJ 07102, USA.
DOI: 10.1080/0020716031000079518
Publication Frequency: 15 issues per year
Published in: journal International Journal of Computer Mathematics, Volume 80, Issue 6 June 2003 , pages 689 - 707
Number of References: 12
Formats available: PDF (English)
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Abstract

We examine in this work the following graph theory problem that arises in neural computations that involve the learning of boolean expressions by studying the asymptotic connectivity properties of $G_lcubn\comma 1/\lpar kn\rpar ^lcub1/2rcubrcub$ random graphs, where k is a fixed positive integer. For an undirected graph $G = \lpar V\comma \; E\rpar $ let $N\lpar X\comma \; Y\rpar = \lcub v \in V - \lpar X \cup Y\rpar \!\mid$ $ \exists x \in X\ \hboxlcubwithrcub\ \lpar v\comma \; x\rpar \in E\rcub $ . For fixed k construct an undirected graph $G = \lpar V\comma \; E\rpar $ such that for all disjoint sets $A\comma \; B \subseteq V$ such that $\vert A \vert = \vert B \vert = k$ , and $C = N\lpar A\comma \; B\rpar \cap N\lpar B\comma \; A\rpar $ , set C is such that $\vert C \vert$ is either exactly k or as close to k as possible. Asymptotic results for large values of k are also presented.
Keywords: Random Graphs; Connectivity Properties; Neural Networks
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