The Development of Small-world Semantic Networks

Perspectives in Neural Computing - Connectionist Models of Learning, Development and Evolution ◽

10.1007/978-1-4471-0281-6_29 ◽

2001 ◽

pp. 293-302

Author(s):

Eric Postma ◽

Alard Roebroeck ◽

Joyca Lacroix

Keyword(s):

Small World ◽

Semantic Networks ◽

World Semantic

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K-clique percolation in free association networks. The mechanism behind the 7 ± 2 law?

10.21203/rs.3.rs-1031550/v1 ◽

2021 ◽

Author(s):

Olga Valba ◽

Alexander Gorsky

Keyword(s):

Community Organization ◽

Preferential Attachment ◽

Free Association ◽

Small World ◽

Semantic Networks ◽

Network Evolution ◽

Cognitive Networks ◽

Qualitative Explanation ◽

Capacity Limit ◽

Clique Percolation

Abstract It is important to reveal the mechanisms of propagation in different cognitive networks. In this study we discuss the k-clique percolation phenomenon on the free association networks including "English Small World of Words project" (SWOW-EN). We compare different semantic networks and networks of free associations for different languages. Surprisingly it turned out that k-clique percolation for all k < k c = (6 − 7) is possible on free association networks of different languages. Our analysis suggests the new universality patterns for a community organization of free association networks. We conjecture that our result can provide the qualitative explanation of the Miller’s 7 ± 2 rule for the capacity limit of working memory. The new model of network evolution extending the preferential attachment is suggested which provides the observed value of k c .

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Small-world Structure in Children’s Featured Semantic Networks

Caspian Journal of Neurological Sciences ◽

10.32598/cjns.7.27.1 ◽

2021 ◽

Vol 7 (4) ◽

pp. 185-192

Author(s):

Somayeh Sadat Hashemikamangar ◽

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Shahriar Gharibzadeh ◽

Fatemeh Bakouie ◽

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...

Keyword(s):

Developmental Psychology ◽

Network Models ◽

Small World ◽

Semantic Networks ◽

Clustering Coefficient ◽

Random Networks ◽

Dense Networks ◽

Global Access ◽

Small World Property ◽

Feature Norms

Background: Knowing the development pattern of children’s language is applicable in developmental psychology. Network models of language are helpful for the identification of these patterns. Objectives: We examined the small-world properties of featured semantic networks of developing children. Materials & Methods: In this longitudinal study, the featured semantic networks of children aged 18-30 months were obtained using R software version 3.5.2 and the igraph software package. The data of 2000 English (British)-speaking children, half boy and half girls, were gathered from existing databases of MCDI (between 2000 and 2007) and McRae feature norms. The growth pattern of these networks was illustrated by graph measures. Comparing these measures with those of the reference random networks, the small-world structure can be examined. Results: To have a comparison between path length and clustering coefficient of featured semantic networks with those of random networks, we computed the Q quotient. The results showed that the values of the Q quotient at 18, 22, 26, and 30 months of age were all more than 1, which confirms the small-world characteristic of the networks. Conclusion: Featured semantic networks of children exhibited a small-world structure, in which there was a local structure in the form of clusters of words. For global access, some words act as bridges connecting semantically distant clusters. These networks possess small-world property from the early months of age. The small-world structure cannot be seen in the less dense networks built with a higher cut-off threshold.

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