A study on the effects of diffusion of information on epidemic spread

In this work, the spread of a contagious disease on a society where the individuals may take precautions is modeled. The primary assumption is that the infected individuals transmit the infection to the susceptible members of the community through direct contact interactions. In the meantime, the susceptibles gather information from the adjacent sites which may lead to taking precautions. The SIR model is used for the diffusion of infection while the Bass equation models the information diffusion. The sociological classification of the individuals indicates that a small percentage of the population takes action immediately after being informed, while the majority expects to see some real advantage of taking action. The individuals are assumed to take two different precautions. The precursory measures are getting vaccinated or trying to avoid direct contact with the neighbors. A weighted average of states of the neighbors leads to the choice of action. The fully connected and scale-free Networks are employed as the underlying network of interactions. The comparison between the simple contagion diffusion and the diffusion of infection in a responsive society showed that a very limited precaution makes a considerable difference in the speed and the size of the spread of illness. Particularly, highly connected hub nodes play an essential role in the reduction of the spread of disease.

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THE GINI COEFFICIENT'S APPLICATION TO GENERAL COMPLEX NETWORKS

Advances in Complex Systems ◽

10.1142/s0219525905000385 ◽

2005 ◽

Vol 08 (01) ◽

pp. 159-167 ◽

Cited By ~ 11

Author(s):

HAI-BO HU ◽

LIN WANG

Keyword(s):

Income Inequality ◽

Complex Networks ◽

Network Structure ◽

Gini Coefficient ◽

Scale Free ◽

The Gini Coefficient ◽

Scale Free Networks ◽

General Complex

The Gini coefficient, which was originally used in microeconomics to describe income inequality, is introduced into the research of general complex networks as a metric on the heterogeneity of network structure. Some parameters such as degree exponent and degree-rank exponent were already defined in the case of scale-free networks also as a metric on the heterogeneity. In scale-free networks, the Gini coefficient is proved to be equivalent to the parameters mentioned above, and moreover, a classification of infinite scale-free networks is given according to the value of the Gini coefficient.

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Deep Learning Based Tobacco Products Classification (Preprint)

10.2196/preprints.14761 ◽

2019 ◽

Author(s):

Murat Taşkıran ◽

Sibel Çimen Yetiş

Keyword(s):

Young People ◽

Feature Vector ◽

Weighted Average ◽

Classification Performance ◽

Accuracy Rate ◽

Tobacco Products ◽

Internet Environment ◽

Performance Rate ◽

Fully Connected

BACKGROUND Various images and videos are uploaded every day or even every second on Instagram. These publicly available images are easily accessible as a result of uncontrolled Internet use by young people and children. Shared images include tobacco products and can be encouraging for young people and children when they are accessible. OBJECTIVE In this study, it is aimed to detect tobacco and tobacco products with various Convolutional Neural Networks (CNNs) and to limit the access of young users to these detected tobacco products over the Internet. METHODS A total of 1607 public images were collected from Instagram, and feature vectors were extracted with various CNNs, which proved to be successful in the competitions and CNN was determined to be proper for detect tobacco products. RESULTS MobileNet gave the highest results 99.1% as weighted average. The feature vector of the input images are extracted with CNNs and classified with the latest fully connected layer. CONCLUSIONS The classification of the tobacco products of 4 different classes was studied by using the networks and the classification performance rate was obtained as 100% for 322 test images via MobileNet. In this way, the content that is encouraging for children can be censored or filtered with a high accuracy rate and a secure Internet environment can be provided.

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Classification of scale-free networks

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.202301299 ◽

2002 ◽

Vol 99 (20) ◽

pp. 12583-12588 ◽

Cited By ~ 245

Author(s):

K.-I. Goh ◽

E. Oh ◽

H. Jeong ◽

B. Kahng ◽

D. Kim

Keyword(s):

Scale Free ◽

Scale Free Networks

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PERFORMANCE AND ROBUSTNESS OF CELLULAR AUTOMATA COMPUTATION ON IRREGULAR NETWORKS

Advances in Complex Systems ◽

10.1142/s0219525907001124 ◽

2007 ◽

Vol 10 (supp01) ◽

pp. 85-110 ◽

Cited By ~ 16

Author(s):

CHRISTIAN DARABOS ◽

MARIO GIACOBINI ◽

MARCO TOMASSINI

Keyword(s):

Information Diffusion ◽

Small World ◽

Superior Performance ◽

Small World Networks ◽

Scale Free ◽

Irregular Networks ◽

Scale Free Networks ◽

The Face ◽

Scale Free Graphs ◽

Synchronization Problems

We investigate the performances of collective task-solving capabilities and the robustness of complex networks of automata using the density and synchronization problems as typical cases. We show by computer simulations that evolved Watts–Strogatz small-world networks have superior performance with respect to several kinds of scale-free graphs. In addition, we show that Watts–Strogatz networks are as robust in the face of random perturbations, both transient and permanent, as configuration scale-free networks, while being widely superior to Barabási–Albert networks. This result differs from information diffusion on scale-free networks, where random faults are highly tolerated by similar topologies.

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Dynamics of tax evasion through an epidemic-like model

International Journal of Modern Physics C ◽

10.1142/s0129183117500231 ◽

2017 ◽

Vol 28 (02) ◽

pp. 1750023 ◽

Cited By ~ 3

Author(s):

Rafael M. Brum ◽

Nuno Crokidakis

Keyword(s):

Social Interactions ◽

Tax Evasion ◽

Nonequilibrium Phase Transition ◽

Epidemic Spreading ◽

Scale Free ◽

Scale Free Networks ◽

Random Complex ◽

Fully Connected ◽

Tax Payers ◽

Fixed Population

In this work, we study a model of tax evasion. We considered a fixed population divided in three compartments, namely honest tax payers, tax evaders and a third class between the mentioned two, which we call susceptibles to become evaders. The transitions among those compartments are ruled by probabilities, similarly to a model of epidemic spreading. These probabilities model social interactions among the individuals, as well as the government’s fiscalization. We simulate the model on fully-connected graphs, as well as on scale-free and random complex networks. For the fully-connected and random graph cases, we observe that the emergence of tax evaders in the population is associated with an active-absorbing nonequilibrium phase transition, that is absent in scale-free networks.

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Deep Learning Based Tobacco Products Classification (Preprint)

10.2196/preprints.14048 ◽

2019 ◽

Author(s):

Murat Taskiran ◽

Sibel Cimen Yetis

Keyword(s):

Young People ◽

Feature Vector ◽

Weighted Average ◽

Classification Performance ◽

Accuracy Rate ◽

Tobacco Products ◽

Internet Environment ◽

Performance Rate ◽

Fully Connected

BACKGROUND Various images and videos are uploaded every day or even every second on Instagram. These publicly available images are easily accessible as a result of uncontrolled Internet use by young people and children. Shared images include tobacco products and can be encouraging for young people and children when they are accessible. OBJECTIVE In this study, it is aimed to detect tobacco and tobacco products with various Convolutional Neural Networks (CNNs). METHODS A total of 1607 public images were collected from Instagram, and feature vectors were extracted with various CNNs, which proved to be successful in the competitions and CNN was determined to be proper for this problem. RESULTS MobileNet gave the highest results 99.1% as weighted average. The feature vector of the input images are extracted with CNNs and classified with the latest fully connected layer. CONCLUSIONS The classification of the tobacco products of 4 different classes was studied by using the networks and the classification performance rate was obtained as 100% for 322 test images via MobileNet. In this way, the content that is encouraging for children can be censored or filtered with a high accuracy rate and a secure Internet environment can be provided.

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Traffic Properties for Stochastic Routing on Scale-Free Networks

IEICE Transactions on Communications ◽

10.1587/transcom.e94.b.1311 ◽

2011 ◽

Vol E94-B (5) ◽

pp. 1311-1322

Author(s):

Yukio HAYASHI ◽

Yasumasa ONO

Keyword(s):

Scale Free ◽

Stochastic Routing ◽

Scale Free Networks

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Dynamics analysis of an online gambling spreading model on scale-free networks

Advances in Difference Equations ◽

10.1186/s13662-020-03165-z ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Yu Kong ◽

Tao Li ◽

Yuanmei Wang ◽

Xinming Cheng ◽

He Wang ◽

...

Keyword(s):

Network Topology ◽

Negative Impact ◽

Online Gambling ◽

Basic Reproductive Number ◽

Reproductive Number ◽

Scale Free ◽

Spreading Model ◽

Spreading Dynamics ◽

Scale Free Networks ◽

Gambling Policy

AbstractNowadays, online gambling has a great negative impact on the society. In order to study the effect of people’s psychological factors, anti-gambling policy, and social network topology on online gambling dynamics, a new SHGD (susceptible–hesitator–gambler–disclaimer) online gambling spreading model is proposed on scale-free networks. The spreading dynamics of online gambling is studied. The basic reproductive number $R_{0}$ R 0 is got and analyzed. The basic reproductive number $R_{0}$ R 0 is related to anti-gambling policy and the network topology. Then, gambling-free equilibrium $E_{0}$ E 0 and gambling-prevailing equilibrium $E_{ +} $ E + are obtained. The global stability of $E_{0}$ E 0 is analyzed. The global attractivity of $E_{ +} $ E + and the persistence of online gambling phenomenon are studied. Finally, the theoretical results are verified by some simulations.

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