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

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 .

An Exploratory Psychometric Network Analysis of Loneliness Scales in a Sample of Older Adults.

Objectives: To examine the relationships within and between commonly used measures ofloneliness to determine the suitability of the measures in older adults. Further, todetermine items of key importance to the measurement of loneliness. Methods: Data wereobtained from 350 older adults via completion of an online survey. Four measures ofloneliness were completed. These were the UCLA Loneliness scale (Version 3), the de JongGierveld Loneliness Scale, the Social and Emotional Loneliness Scale for Adults (ShortVersion) and a direct measure of loneliness. Results: Analysis via a regularized partialcorrelation network and via clique percolation revealed that only the SELSA-Sencompassed loneliness relating to deficits in social, family and romantic relationships. Theremaining measures tapped mostly into social loneliness alone. The direct measure ofloneliness had the strongest connection to the UCLA item-4 and the de Jong Giervelditem-1 exhibited the strongest bridge centrality, being a member of the most clusters.Discussion: The results indicate that should researchers be interested in assessingloneliness resulting from specific relationships, then the SELSA-S would be the mostsuitable measure. Whereas the other measures are suitable for assessing loneliness moregenerally. The results further suggest that the de Jong Gierveld item-1 may be a moresuitable direct measure of loneliness than that currently employed as it taps into a greaternumber of relationships.

Bridging brain and cognition: A multilayer network analysis of brain structural covariance and general intelligence in a developmental sample of struggling learners

ABSTRACTNetwork analytic methods that are ubiquitous in other areas, such as systems neuroscience, have recently been used to test network theories in psychology, including intelligence research. The network or mutualism theory of intelligence proposes that the statistical associations among cognitive abilities (e.g. specific abilities such as vocabulary or memory) stem from causal relations among them throughout development. In this study, we used network models (specifically LASSO) of cognitive abilities and brain structural covariance (grey and white matter) to simultaneously model brain-behavior relationships essential for general intelligence in a large (behavioral, N=805; cortical volume, N=246; fractional anisotropy, N=165), developmental (ages 5-18) cohort of struggling learners (CALM). We found that mostly positive, small partial correlations pervade both our cognitive and neural networks. Moreover, calculating node centrality (absolute strength and bridge strength) and using two separate community detection algorithms (Walktrap and Clique Percolation), we found convergent evidence that subsets of both cognitive and neural nodes play an intermediary role between brain and behavior. We discuss implications and possible avenues for future studies.

Community cores expansion for overlapping community detection in complex networks

The identification of communities has attracted considerable attentions in the last few years. We propose a novel heuristic algorithm for overlapping community detection based on community cores in complex networks. We introduce a novel clique percolation algorithm and maximize cliques in the finding overlapping communities (node covers) in graphs. We show how vertices can be used to quantify types of local structure presented in a community and identify group nodes that have similar roles in relation to their neighbors. We compare the approach with other three common algorithms in the analysis of the Zachary’s karate club network and the dolphins network. Experimental results in real-world and synthetic datasets (Lancichinetti–Fortunato–Radicchi (LFR) benchmark networks [A. Lancichinetti and S. Fortunato, Phys. Rev. E 80 (2009) 016118]) demonstrate the model has scalability and is well behaved.