scholarly journals Small-world Structure in Children’s Featured Semantic Networks

2021 ◽  
Vol 7 (4) ◽  
pp. 185-192
Author(s):  
Somayeh Sadat Hashemikamangar ◽  
◽  
Shahriar Gharibzadeh ◽  
Fatemeh Bakouie ◽  
◽  
...  
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.

THE SMALL-WORLD PROPERTY IN NETWORKS GROWING BY ACTIVE EDGES

2011 ◽  
Vol 14 (06) ◽  
pp. 853-869 ◽  
Author(s):  
PHILIPPE J. GIABBANELLI
Keyword(s):  
Growth Mechanism ◽  
Average Distance ◽  
Network Models ◽  
Small World ◽  
Clustering Coefficient ◽  
Small World Networks ◽  
Fractal Approach ◽  
Small World Property ◽  
Complex Network Models ◽  
Similar Growth

In the last three years, we have witnessed an increasing number of complex network models based on a 'fractal' approach, in which parts of the network are repeatedly replaced by a given pattern. Our focus is on models that can be defined by repeatedly adding a pattern network to selected edges, called active edges. We prove that when a pattern network has at least two active edges, then the resulting network has an average distance at most logarithmic in the number of nodes. This suggests that real-world networks based on a similar growth mechanism are likely to have small average distance. We provide an estimate of the clustering coefficient and verify its accuracy using simulations. Using numerous examples of simple patterns, our simulations show various ways to generate small-world networks. Finally, we discuss extensions to our framework encompassing probabilistic patterns and active subnetworks.

A NETWORK MODEL GENERATED FROM THE RECURSIVE GRAPH BASED ON POLYGON

2013 ◽  
Vol 24 (09) ◽  
pp. 1350062 ◽  
Author(s):  
YUANYUAN SUN ◽  
KAINING HOU ◽  
YUJIE ZHAO
Keyword(s):  
Real Life ◽  
Network Models ◽  
Small World ◽  
Mapping Method ◽  
Clustering Coefficient ◽  
Research Fields ◽  
Network Diameter ◽  
Degree Correlations ◽  
New Perspective ◽  
Small World Property

The study of network models is one of the most challenging research fields among the studies of complex networks, which have been the hot research topics in recent decades. In this paper, we construct a deterministic network by a mapping method based on a recursive graph, and analyze its topological characteristics, including degree distribution, clustering coefficient, network diameter, average path length and degree correlations. We obtain that this network has the small-world property and positive correlation. The network modeling as we present gives a new perspective on networks, and helps to understand better the evolutions of the real-life systems, making it possible to explore the complexity of complex systems.

scholarly journals Studies on the Decrease Mechanisms of Typical Complex Networks

2021 ◽  
Author(s):  
Yuhu Qiu ◽  
Tianyang Lyu ◽  
Xizhe Zhang ◽  
Ruozhou Wang
Keyword(s):  
Average Length ◽  
Shortest Paths ◽  
Real Life ◽  
Network Models ◽  
Small World ◽  
Clustering Coefficient ◽  
Network Growth ◽  
Scale Free ◽  
Complex Network Models ◽  
High Degree

Network decrease caused by the removal of nodes is an important evolution process that is paralleled with network growth. However, many complex network models usually lacked a sound decrease mechanism. Thus, they failed to capture how to cope with decreases in real life. The paper proposed decrease mechanisms for three typical types of networks, including the ER networks, the WS small-world networks and the BA scale-free networks. The proposed mechanisms maintained their key features in continuous and independent decrease processes, such as the random connections of ER networks, the long-range connections based on nearest-coupled network of WS networks and the tendency connections and the scale-free feature of BA networks. Experimental results showed that these mechanisms also maintained other topology characteristics including the degree distribution, clustering coefficient, average length of shortest-paths and diameter during decreases. Our studies also showed that it was quite difficult to find an efficient decrease mechanism for BA networks to withstand the continuous attacks at the high-degree nodes, because of the unequal status of nodes.

scholarly journals Directed random geometric graphs

2019 ◽  
Vol 7 (5) ◽  
pp. 792-816
Author(s):  
Jesse Michel ◽  
Sushruth Reddy ◽  
Rikhav Shah ◽  
Sandeep Silwal ◽  
Ramis Movassagh
Keyword(s):  
Real World ◽  
Word Association ◽  
Graph Model ◽  
Small World ◽  
Geometric Graph ◽  
Clustering Coefficient ◽  
Random Geometric Graph ◽  
Random Geometric Graphs ◽  
Scale Free ◽  
Small World Property

Abstract Many real-world networks are intrinsically directed. Such networks include activation of genes, hyperlinks on the internet and the network of followers on Twitter among many others. The challenge, however, is to create a network model that has many of the properties of real-world networks such as power-law degree distributions and the small-world property. To meet these challenges, we introduce the Directed Random Geometric Graph (DRGG) model, which is an extension of the random geometric graph model. We prove that it is scale-free with respect to the indegree distribution, has binomial outdegree distribution, has a high clustering coefficient, has few edges and is likely small-world. These are some of the main features of aforementioned real-world networks. We also empirically observed that word association networks have many of the theoretical properties of the DRGG model.

A NEW DYNAMIC COMMUNITY MODEL FOR SOCIAL NETWORKS

2014 ◽  
Vol 25 (02) ◽  
pp. 1350088 ◽  
Author(s):  
ZHE-MING LU ◽  
ZHEN WU ◽  
SHI-ZE GUO ◽  
ZHE CHEN ◽  
GUANG-HUA SONG
Keyword(s):  
Social Networks ◽  
Average Distance ◽  
Small World ◽  
Clustering Coefficient ◽  
Community Model ◽  
Network Diameter ◽  
Proposed Model ◽  
Small World Property ◽  
Two Parameters ◽  
Dynamic Community

In this paper, based on the phenomenon that individuals join into and jump from the organizations in the society, we propose a dynamic community model to construct social networks. Two parameters are adopted in our model, one is the communication rate Pa that denotes the connection strength in the organization and the other is the turnover rate Pb, that stands for the frequency of jumping among the organizations. Based on simulations, we analyze not only the degree distribution, the clustering coefficient, the average distance and the network diameter but also the group distribution which is closely related to their community structure. Moreover, we discover that the networks generated by the proposed model possess the small-world property and can well reproduce the networks of social contacts.

Abstract 663: Metabolomics And Correlation Network Analysis Of Hypertension

Hypertension ◽  
2014 ◽  
Vol 64 (suppl_1) ◽  
Author(s):  
zhongmin tian ◽  
le wang ◽  
entai hou ◽  
qiong sun
Keyword(s):  
Amino Acids ◽  
Comprehensive Evaluation ◽  
Small World ◽  
Clustering Coefficient ◽  
Correlation Network ◽  
Beta Alanine ◽  
Topological Parameters ◽  
Correlation Networks ◽  
Metabolomics Study ◽  
Small World Property

The awareness, treatment and controls rates of hypertension for people in their 20s and 30s age are much lower than average. In this paper, a GC/MS based metabolomics study was performed in plasma of young hypertensive men and age-matched normal ones. Correlations of the identified metabolites were analyzed and visualized. A systematic correlation network was constructed with the significance of correlation coefficient setting at threshold of 0.6. Glycine, lysine, cystine and beta-alanine were selected as the most important nodes of the network, with high values of degree. A relatively short average path length and high clustering coefficient suggested a small-world property of the network. Moreover, differential metabolites in young hypertensive men were used to construct a core correlation network for further understanding. Four hubs (lysine, glycine, cystine and tryptophan) were confirmed by a comprehensive evaluation of three centrality indices. The statistical and topological parameters of the network indicated that local disturbance to hubs would rapidly transfer to the whole network. These results demonstrated that the distinct metabolic profiles of young hypertensive men might be due to perturbation of the biosynthesis pathway of amino acids. Integrated analyses of metabolomics and correlation networks would provide a broadened window for further understanding of hypertension. Key Words: metabolomics; hypertension; correlation network; amino acids; statistical and topological characteristics; centrality indices; hubs

TRAPPING ON WEIGHTED TETRAHEDRON KOCH NETWORKS WITH SMALL-WORLD PROPERTY

Fractals ◽  
2014 ◽  
Vol 22 (01n02) ◽  
pp. 1450006 ◽  
Author(s):  
MEIFENG DAI ◽  
QI XIE ◽  
LIFENG XI
Keyword(s):  
Small World ◽  
Clustering Coefficient ◽  
Weight Factor ◽  
First Passage ◽  
Mean First Passage Times ◽  
Weighted Clustering ◽  
Self Similar ◽  
Small World Property ◽  
Analytical Expressions ◽  
Passage Times

In this paper, we present weighted tetrahedron Koch networks depending on a weight factor. According to their self-similar construction, we obtain the analytical expressions of the weighted clustering coefficient and average weighted shortest path (AWSP). The obtained solutions show that the weighted tetrahedron Koch networks exhibits small-world property. Then, we calculate the average receiving time (ART) on weighted-dependent walks, which is the sum of mean first-passage times (MFPTs) for all nodes absorpt at the trap located at a hub node. We find that the ART exhibits a sublinear or linear dependence on network order.

scholarly journals Characterizing several properties of high-dimensional random Apollonian networks

2020 ◽  
Vol 8 (4) ◽  
Author(s):  
Panpan Zhang
Keyword(s):  
Small World ◽  
Wiener Index ◽  
Clustering Coefficient ◽  
Probabilistic Methods ◽  
High Dimensional ◽  
Analytic Combinatorics ◽  
Local Clustering ◽  
Clustering Property ◽  
Small World Property ◽  
Local Clustering Coefficient

Abstract In this article, we investigate several properties of high-dimensional random Apollonian networks, including two types of degree profiles, the small-world effect (clustering property), sparsity and three distance-based metrics. The characterizations of the degree profiles are based on several rigorous mathematical and probabilistic methods, such as a two-dimensional mathematical induction, analytic combinatorics and Pólya urns, etc. The small-world property is uncovered by a well-developed measure—local clustering coefficient and the sparsity is assessed by a proposed Gini index. Finally, we look into three distance-based properties; they are total depth, diameter and Wiener index.

scholarly journals The Structure of Plant-Animal Mutualistic Networks

2013 ◽  
Author(s):  
Jordi Bascompte ◽  
Pedro Jordano
Keyword(s):  
Small Groups ◽  
Path Length ◽  
Small World ◽  
Building Blocks ◽  
Clustering Coefficient ◽  
Short Path Length ◽  
Morphological Convergence ◽  
Mutualistic Networks ◽  
Small World Property ◽  
Asymmetric Interactions

This chapter discusses the structure of mutualistic networks. Despite their apparent complexity, mutualistic networks show repeated, universal structural patterns independent of species composition, size, and other ecological details. First, mutualistic networks are very heterogeneous: whereas the majority of species have only one or a few interactions, a few species are much more connected than expected by chance. Second, mutualistic networks are highly nested, that is, specialists interact with well-defined subsets of the species generalists interact with. Third, mutualistic networks are built on weak, asymmetric interactions among species. Fourth, mutualistic networks have a strong small-world property; that is, they simultaneously have a short path length among any pair of species and a high clustering coefficient. Finally, these networks are significantly modular—that is, there are small groups of species with morphological convergence of traits that interact strongly among themselves and more loosely with species from other modules. These modules can be regarded as the basic building blocks of mutualistic networks and their coevolutionary units.

STOCHASTIC EVOLVING MODEL FOR COMPLEX NETWORKS

2004 ◽  
Vol 18 (23) ◽  
pp. 1157-1164 ◽  
Author(s):  
HYUN-JOO KIM ◽  
YEON-MU CHOI ◽  
JIN MIN KIM
Keyword(s):  
Control Parameter ◽  
Small World ◽  
Network Size ◽  
Clustering Coefficient ◽  
Scale Free ◽  
Hierarchical Topology ◽  
Free Behavior ◽  
Small World Property ◽  
Evolving Model ◽  
High Degree

We introduce an evolving complex network model, where a new vertex is added and new edges between already existing vertices are added with a control parameter p. The model shows the characteristics of real networks such as small-world property, high degree of clustering, scale-free behavior in degree distribution, and hierarchical topology. We obtain the various values of degree exponent γ in the range 2<γ≤3 by adjusting the parameter p and find that the degree exponent decreases logarithmically with p. In addition, the clustering coefficient is tunable by changing the control parameter p, and the average path length L is proportional to ln ( ln N) with nonzero p, where N is the network size.

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