scholarly journals Inferring Online Relationships from User Characteristics

2018 ◽  
Vol 16 (2) ◽  
pp. 71-93
Author(s):  
Suwimon VONGSINGTHONG ◽  
Sirapat BOONKRONG ◽  
Herwig UNGER
Keyword(s):  
Psychological Functioning ◽  
Clustering Algorithm ◽  
Structural Evolution ◽  
Small World ◽  
Centrality Measures ◽  
User Characteristics ◽  
Scale Free ◽  
Network Properties ◽  
The Individual ◽  
Behavior Network

A new approach to understanding human online behavior in regard to psychological functioning is proposed through the developed user’s activity model incorporating the influences of social behavior, network, and content. Microscopic levels of user characteristics induced by personality traits were interpolated as interaction rules, whilst an unsupervised clustering algorithm was applied to penetrate the individual complexity. Temporal behavior of disparate users was mimicked, and streaming network data was generated and computationally analyzed. A comprehensive understanding of how individuality, friendship, and varying temperaments dramatically reshaped the networks was gained from insight synthesis of network properties characterized by small-world, scale-free, and centrality measures. Evidence illustrates that users with high extraversion possess high numbers of friends and spread massive information, while high conscientious and high intellect users are seriously discreet in accepting friends and often produce influential content. These results not only provide a wealth of challenges for product recommendation, network structure optimization, and design, but also are useful for the prediction of future network structural evolution.

scholarly journals Characterizing the Structure of the Railway Network in China: A Complex Weighted Network Approach

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Weiwei Cao ◽  
Xiangnan Feng ◽  
Jianmin Jia ◽  
Hong Zhang
Keyword(s):  
Structural Characteristics ◽  
Flow Distribution ◽  
Small World ◽  
Centrality Measures ◽  
Railway Network ◽  
Weighted Network ◽  
Scale Free ◽  
Topological Measures ◽  
Free Network

Understanding the structure of the Chinese railway network (CRN) is crucial for maintaining its efficiency and planning its future development. To advance our knowledge of CRN, we modeled CRN as a complex weighted network and explored the structural characteristics of the network via statistical evaluations and spatial analysis. Our results show CRN as a small-world network whose train flow obeys power-law decaying, demonstrating that CRN is a mature transportation infrastructure with a scale-free structure. CRN also shows significant spatial heterogeneity and hierarchy in its regionally uneven train flow distribution. We then examined the nodal centralities of CRN using four topological measures: degree, strength, betweenness, and closeness. Nodal degree is positively correlated with strength, betweenness, and closeness. Unlike the common feature of a scale-free network, the most connected nodes in CRN are not necessarily the most central due to underlying geographical, political, and socioeconomic factors. We proposed an integrated measure based on the four centrality measures to identify the global role of each node and the multilayer structure of CRN and confirm that stable connections hold between different layers of CRN.

scholarly journals Network frailty and the geometry of herd immunity

2006 ◽  
Vol 273 (1602) ◽  
pp. 2743-2748 ◽  
Author(s):  
Matthew J Ferrari ◽  
Shweta Bansal ◽  
Lauren A Meyers ◽  
Ottar N Bjørnstad
Keyword(s):  
Infectious Disease ◽  
Herd Immunity ◽  
Structural Evolution ◽  
Small World ◽  
Network Evolution ◽  
Structured Populations ◽  
Susceptible Population ◽  
Scale Free ◽  
Contact Patterns ◽  
Community Protection

The spread of infectious disease through communities depends fundamentally on the underlying patterns of contacts between individuals. Generally, the more contacts one individual has, the more vulnerable they are to infection during an epidemic. Thus, outbreaks disproportionately impact the most highly connected demographics. Epidemics can then lead, through immunization or removal of individuals, to sparser networks that are more resistant to future transmission of a given disease. Using several classes of contact networks—Poisson, scale-free and small-world—we characterize the structural evolution of a network due to an epidemic in terms of frailty (the degree to which highly connected individuals are more vulnerable to infection) and interference (the extent to which the epidemic cuts off connectivity among the susceptible population that remains following an epidemic). The evolution of the susceptible network over the course of an epidemic differs among the classes of networks; frailty, relative to interference, accounts for an increasing component of network evolution on networks with greater variance in contacts. The result is that immunization due to prior epidemics can provide greater community protection than random vaccination on networks with heterogeneous contact patterns, while the reverse is true for highly structured populations.

scholarly journals Comparison and Analysis of Network Construction Methods for Seismicity Based on Complex Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xuan He ◽  
Syed Bilal Hussain Shah ◽  
Bo Wei ◽  
Zheng Liu
Keyword(s):  
Small World ◽  
Network Construction ◽  
Scale Free ◽  
Construction Methods ◽  
Degree Correlation ◽  
Network Properties ◽  
Comparison And Analysis ◽  
Similarities And Differences ◽  
First Time ◽  
Predictive Indicator

The approach of the complex network has well described seismic complex systems. In this paper, this is the first time three classical network construction methods for seismicity are compared. By using the same dataset from the Southern California Seismic Network, three networks are constructed. They all present the scale-free, small-world properties, a strength-degree correlation, and an assortative mixing feature. However, they show some differences in the hierarchical clustering feature. On observing the evolution results, three measures show a similar correlation with seismicity dynamics, but one measure shows a different result. These results show that different network construction methods will present some similarities and differences in network properties. This situation needs to be considered, especially when discussing a predictive indicator of seismicity.

scholarly journals Cross-Shareholdings Structural Characteristic and Evolution Analysis Based on Complex Network

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Xiaohong Chang ◽  
Haiyun Wang
Keyword(s):  
Complex Network ◽  
Network Structure ◽  
Practical Importance ◽  
Small World ◽  
Clustering Coefficient ◽  
Policy Makers ◽  
Scale Free ◽  
Network Properties ◽  
The Cross ◽  
Cross Shareholdings

This study depicts the network morphology of firms which establish ties through cross-shareholdings by the theory of complex network analysis method. It calculates some complex network properties of the cross-shareholdings network and analyzes the evolution law of network structure in nearly 7 years. The network clearly displays small world properties and scale-free properties. The cross-shareholdings network average path length and clustering coefficient is with a small amplitude fluctuation; the network structure is relatively stable. Such a study is of practical importance and could provide opportunities for policy makers to improve the performance of the cross-shareholdings network.

scholarly journals A Survey on the Role of Centrality as Seed Nodes for Information Propagation in Large Scale Network

2021 ◽  
Vol 2 (3) ◽  
pp. 1-25
Author(s):  
Paramita Dey ◽  
Subhayan Bhattacharya ◽  
Sarbani Roy
Keyword(s):  
Large Scale ◽  
Similarity Measures ◽  
Small World ◽  
Centrality Measures ◽  
Node Degree ◽  
Information Propagation ◽  
Stream Data ◽  
Scale Free ◽  
Large Scale Network ◽  
Propagation Methods

From the popular concept of six-degree separation, social networks are generally analyzed in the perspective of small world networks where centrality of nodes play a pivotal role in information propagation. However, working with a large dataset of a scale-free network (which follows power law) may be different due to the nature of the social graph. Moreover, the derivation of centrality may be difficult due to the computational complexity of identifying centrality measures. This study provides a comprehensive and extensive review and comparison of seven centrality measures (clustering coefficients, Node degree, K-core, Betweenness, Closeness, Eigenvector, PageRank) using four information propagation methods (Breadth First Search, Random Walk, Susceptible-Infected-Removed, Forest Fire). Five benchmark similarity measures (Tanimoto, Hamming, Dice, Sorensen, Jaccard) have been used to measure the similarity between the seed nodes identified using the centrality measures with actual source seeds derived through Google's LargeStar-SmallStar algorithm on Twitter Stream Data. MapReduce has been utilized for identifying the seed nodes based on centrality measures and for information propagation simulation. It is observed that most of the centrality measures perform well compared to the actual source in the initial stage but are saturated after a certain level of influence maximization in terms of both affected nodes and propagation level.

Exploring the spiral of silence in adjustable social networks

2015 ◽  
Vol 26 (11) ◽  
pp. 1550125 ◽  
Author(s):  
Yue Wu ◽  
Ya-Jun Du ◽  
Xian-Yong Li ◽  
Xiao-Liang Chen
Keyword(s):  
Public Opinion ◽  
Initial Conditions ◽  
Freedom Of Expression ◽  
Small World ◽  
Node Degree ◽  
Spiral Of Silence ◽  
Scale Free ◽  
The Status ◽  
The Spiral Of Silence ◽  
The Individual

This study extends the understanding of the spiral of silence theory by taking into account four factors, including the topology of networks, the time factor of information transmission, the node degree of individuals and the freedom of expression. Simulation experiments analyze the silencers, public opinion in steady state and relaxation time in small-world networks, scale-free networks and community-structured networks by adjusting the initial conditions. Results highlight that individuals are easier to keep silent in scale-free network, especially when the individual with big degree and minority opinion starts the discussion. Conversely, there are only a few individuals keep silent in the community-structured network when the two communities hold opposite opinions. Moreover, the number of silencers grows as the degree of coupling increases, and it decreases as the freedom of expression goes up. By analyzing the public opinion evolution, we also find some important conditions, such as the network topology, the potential public opinion distribution, and the status and sides of the first speaker, can drive the minority reversal.

Analysis of Oncogene Protein Structure Using Small World Network Concept

2020 ◽  
Vol 15 (7) ◽  
pp. 732-740
Author(s):  
Neetu Kumari ◽  
Anshul Verma
Keyword(s):  
Amino Acids ◽  
Protein Structure ◽  
Degree Distribution ◽  
Protein Structures ◽  
Small World ◽  
Extreme Condition ◽  
Centrality Measures ◽  
Small World Network ◽  
Network Concept ◽  
Oncogene Protein

Background: The basic building block of a body is protein which is a complex system whose structure plays a key role in activation, catalysis, messaging and disease states. Therefore, careful investigation of protein structure is necessary for the diagnosis of diseases and for the drug designing. Protein structures are described at their different levels of complexity: primary (chain), secondary (helical), tertiary (3D), and quaternary structure. Analyzing complex 3D structure of protein is a difficult task but it can be analyzed as a network of interconnection between its component, where amino acids are considered as nodes and interconnection between them are edges. Objective: Many literature works have proven that the small world network concept provides many new opportunities to investigate network of biological systems. The objective of this paper is analyzing the protein structure using small world concept. Methods: Protein is analyzed using small world network concept, specifically where extreme condition is having a degree distribution which follows power law. For the correct verification of the proposed approach, dataset of the Oncogene protein structure is analyzed using Python programming. Results: Protein structure is plotted as network of amino acids (Residue Interaction Graph (RIG)) using distance matrix of nodes with given threshold, then various centrality measures (i.e., degree distribution, Degree-Betweenness correlation, and Betweenness-Closeness correlation) are calculated for 1323 nodes and graphs are plotted. Conclusion: Ultimately, it is concluded that there exist hubs with higher centrality degree but less in number, and they are expected to be robust toward harmful effects of mutations with new functions.

Top Museums on Instagram

2019 ◽  
Vol 3 (2) ◽  
pp. 18-42 ◽  
Author(s):  
Vasiliki G. Vrana ◽  
Dimitrios A. Kydros ◽  
Evangelos C. Kehris ◽  
Anastasios-Ioannis T. Theocharidis ◽  
George I. Kavavasilis
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Small World ◽  
Marketing Strategies ◽  
The Other ◽  
Small World Network ◽  
Scale Free ◽  
Centrality Metrics ◽  
Photo Sharing ◽  
Visualization Algorithms

Pictures speak louder than words. In this fast-moving world where people hardly have time to read anything, photo-sharing sites become more and more popular. Instagram is being used by millions of people and has created a “sharing ecosystem” that also encourages curation, expression, and produces feedback. Museums are moving quickly to integrate Instagram into their marketing strategies, provide information, engage with audience and connect to other museums Instagram accounts. Taking into consideration that people may not see museum accounts in the same way that the other museum accounts do, the article first describes accounts' performance of the top, most visited museums worldwide and next investigates their interconnection. The analysis uses techniques from social network analysis, including visualization algorithms and calculations of well-established metrics. The research reveals the most important modes of the network by calculating the appropriate centrality metrics and shows that the network formed by the museum Instagram accounts is a scale–free small world network.

STABILITY OF TWO TYPICAL COMPLEX DYNAMICAL NETWORKS

2008 ◽  
Vol 22 (05) ◽  
pp. 553-560 ◽  
Author(s):  
WU-JIE YUAN ◽  
XIAO-SHU LUO ◽  
PIN-QUN JIANG ◽  
BING-HONG WANG ◽  
JIN-QING FANG
Keyword(s):  
Numerical Simulation ◽  
Dissipative System ◽  
Small World ◽  
Complex Dynamical Networks ◽  
Dynamical Networks ◽  
Scale Free ◽  
Scale Free Networks ◽  
General Complex ◽  
The Stability ◽  
Theoretical Results

When being constructed, complex dynamical networks can lose stability in the sense of Lyapunov (i. s. L.) due to positive feedback. Thus, there is much important worthiness in the theory and applications of complex dynamical networks to study the stability. In this paper, according to dissipative system criteria, we give the stability condition in general complex dynamical networks, especially, in NW small-world and BA scale-free networks. The results of theoretical analysis and numerical simulation show that the stability i. s. L. depends on the maximal connectivity of the network. Finally, we show a numerical example to verify our theoretical results.

CLUSTERING USING AN IMPROVED HYBRID GENETIC ALGORITHM

2007 ◽  
Vol 16 (06) ◽  
pp. 919-934
Author(s):  
YONGGUO LIU ◽  
XIAORONG PU ◽  
YIDONG SHEN ◽  
ZHANG YI ◽  
XIAOFENG LIAO
Keyword(s):  
Genetic Algorithm ◽  
Clustering Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Sum Of Squares ◽  
Clustering Methods ◽  
Clustering Problem ◽  
Mutation Operation ◽  
Iteration Methods ◽  
Genetic Clustering ◽  
The Individual

In this article, a new genetic clustering algorithm called the Improved Hybrid Genetic Clustering Algorithm (IHGCA) is proposed to deal with the clustering problem under the criterion of minimum sum of squares clustering. In IHGCA, the improvement operation including five local iteration methods is developed to tune the individual and accelerate the convergence speed of the clustering algorithm, and the partition-absorption mutation operation is designed to reassign objects among different clusters. By experimental simulations, its superiority over some known genetic clustering methods is demonstrated.

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