scholarly journals A Dynamic Microblog Network and Information Dissemination in “@” Mode

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Mingsheng Tang ◽  
Xinjun Mao ◽  
Shuqiang Yang ◽  
Huiping Zhou
Keyword(s):  
Decay Rate ◽  
Network Model ◽  
Information Diffusion ◽  
Information Dissemination ◽  
Random Network ◽  
Clustering Coefficient ◽  
Information Spread ◽  
Rate Information ◽  
City Model ◽  
High Degree

Social media, especially the microblogs, emerge as a part of our daily life and become a key way to information spread. Thus, information dissemination in the microblog became a research hotspot. Based on some principles that are summarized from the microblog users’ behaviors, this paper proposes a dynamic microblog network model. Through simulations this network has the features of periodicity of average degree, high clustering coefficient, high degree of modularity, and community. Besides, an information dissemination model through “@” in the microblog has been presented. With the microblog network model and the zombie-city model, this paper has modelled an artificial microblog and has simulated the information dissemination in the artificial microblog with different scenes. Therefore, some interesting findings have been presented. (1) Due to a better connectivity, information could spread widely in a random network; (2) information spreads more quickly in a stable microblog network; (3) the decay rate of the relationships will have an effect on information dissemination; that is, with a lower decay rate, information spreads more quickly and widely; (4) the higher active level of users in microblog could promote information spread widely and quickly; (5) the “@” mode of information dissemination makes a high modularity of the information diffusion network.

Mechanism analysis of competitive information asynchronous dissemination on social networks

2019 ◽  
Vol 30 (11) ◽  
pp. 1950094 ◽  
Author(s):  
Jianye Yu ◽  
Junjie Lv ◽  
Yuanzhuo Wang ◽  
Jingyuan Li
Keyword(s):  
Social Networks ◽  
Information Diffusion ◽  
Information Dissemination ◽  
Evolutionary Game ◽  
Real Data ◽  
Game Model ◽  
Mechanism Analysis ◽  
Sina Weibo ◽  
Human Behaviors ◽  
Information Spread

Information dissemination groups, especially those disseminating the same kind of information such as advertising, product promotion, etc., compete with each other when their information spread on social networks. Most of the existing methods analyze the dissemination mechanism mainly upon the information itself without considering human characteristics, e.g. relation networks, cooperation/defection, etc. In this paper, we introduce a framework of social evolutionary game (SEG) to investigate the influence of human behaviors in competitive information dissemination. Coordination game is applied to represent human behaviors in the competition of asynchronous information diffusion. We perform a series of simulations through a specific game model to analyze the mechanism and factors of information diffusion, and show that when the benefits of competitive information is around 1.2 times of the original one, it can compensate the loss of reputation caused by the change of strategy. Furthermore, through experiments on a dataset of two films on Sina Weibo, we described the mechanism of competition evolution over real data of social network, and validated the effectiveness of our model.

Large Network Model of Complex Systems

2010 ◽  
Vol 26-28 ◽  
pp. 1036-1039
Author(s):  
Cai Feng Du
Keyword(s):  
Differential Equation ◽  
Complex Systems ◽  
Network Model ◽  
Random Network ◽  
Mean Field ◽  
Field Method ◽  
Clustering Coefficient ◽  
Large Network

We proposed a random network model to describe complex systems. The model is solved exactly by mean-field method and differential equation. We demonstrated the triangle distribution firstly to calculate the clustering coefficient. When the size of the network tends to infinity, the model has high clustering coefficient.

Random graphs

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Random Graph ◽  
Random Graphs ◽  
Large Scale ◽  
Random Network ◽  
Graph Model ◽  
Clustering Coefficient ◽  
Giant Component ◽  
Random Graph Model ◽  
Definition Of ◽  
Average Size

An introduction to the mathematics of the Poisson random graph, the simplest model of a random network. The chapter starts with a definition of the model, followed by derivations of basic properties like the mean degree, degree distribution, and clustering coefficient. This is followed with a detailed derivation of the large-scale structural properties of random graphs, including the position of the phase transition at which a giant component appears, the size of the giant component, the average size of the small components, and the expected diameter of the network. The chapter ends with a discussion of some of the shortcomings of the random graph model.

scholarly journals Sublinear domination and core–periphery networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marios Papachristou
Keyword(s):  
Network Model ◽  
Power Law ◽  
Real World ◽  
Random Network ◽  
Small World ◽  
The Core ◽  
Entire Network

AbstractIn this paper we devise a generative random network model with core–periphery properties whose core nodes act as sublinear dominators, that is, if the network has n nodes, the core has size o(n) and dominates the entire network. We show that instances generated by this model exhibit power law degree distributions, and incorporates small-world phenomena. We also fit our model in a variety of real-world networks.

The Structure of Ion-Implanted Amorphous Silicon

1998 ◽  
Vol 540 ◽  
Author(s):  
J. M. Gibson ◽  
J-Y. Cheng ◽  
P. Voyles ◽  
M.M.J. TREACY ◽  
D.C. Jacobson
Keyword(s):  
Amorphous Silicon ◽  
Network Model ◽  
Random Network ◽  
Amorphous Semiconductors ◽  
Range Order ◽  
Medium Range Order ◽  
Medium Range ◽  
Large Heat ◽  
Continuous Random Network ◽  
Ion Implanted

AbstractUsing fluctuation microscopy, we show that ion-implanted amorphous silicon has more medium-range order than is expected from the continuous random network model. From our previous work on evaporated and sputtered amorphous silicon, we conclude that the structure is paracrystalline, i.e. it possesses crystalline-like order which decays with distance from any point. The observation might pose an explanation for the large heat of relaxation that is evolved by ion-implanted amorphous semiconductors.

Evaluation of effective thermal conductivity of unsaturated granular materials using random network model

Geothermics ◽  
2017 ◽  
Vol 67 ◽  
pp. 76-85 ◽  
Author(s):  
Chulho Lee ◽  
Li Zhuang ◽  
Dongseop Lee ◽  
Seokjae Lee ◽  
In-Mo Lee ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Effective Thermal Conductivity ◽  
Granular Materials ◽  
Network Model ◽  
Random Network

Estimation of Effective Thermal and Mechanical Properties of Particulate Thermal Interface Materials (TIMs) by a Random Network Model

2017 ◽  
Author(s):  
Pavan Kumar Vaitheeswaran ◽  
Ganesh Subbarayan
Keyword(s):  
Mechanical Properties ◽  
Network Model ◽  
Thermal Stresses ◽  
Volume Fraction ◽  
Random Network ◽  
Thermal Interface Materials ◽  
Thermal And Mechanical Properties ◽  
Thermal Interface ◽  
Classical Models ◽  
Interface Materials

Particulate thermal interface materials (TIMs) are commonly used to transport heat from chip to heat sink. While high thermal conductance is achieved by large volume loadings of highly conducting particles in a compliant matrix, small volume loadings of stiff particles will ensure reduced thermal stresses in the brittle silicon device. Developing numerical models to estimate effective thermal and mechanical properties of TIM systems would help optimize TIM performance with respect to these conflicting requirements. Classical models, often based on single particle solutions or regular arrangement of particles, are insufficient as real-life TIM systems contain a distriubtion of particles at high volume fractions, where classical models are invalid. In our earlier work, a computationally efficient random network model was developed to estimate the effective thermal conductivity of TIM systems [1,2]. This model is extended in this paper to estimate the effective elastic modulus of TIMs. Realistic microstructures are simulated and analyzed using the proposed method. Factors affecting the modulus (volume fraction and particle size distribution) are also studied.

scholarly journals Influence Model of User Behavior Characteristics on Information Dissemination

2016 ◽  
Vol 11 (2) ◽  
pp. 209 ◽  
Author(s):  
Shao Chun Han ◽  
Yun Liu ◽  
Hui Ling Chen ◽  
Zhen Jiang Zhang
Keyword(s):  
Human Behavior ◽  
Information Diffusion ◽  
Statistical Physics ◽  
Information Dissemination ◽  
User Behavior ◽  
Activity Patterns ◽  
Propagation Model ◽  
Propagation Time ◽  
Behavior Characteristics ◽  
The Impact

Quantitative analysis on human behavior, especially mining and modeling temporal and spatial regularities, is a common focus of statistical physics and complexity sciences. The in-depth understanding of human behavior helps in explaining many complex socioeconomic phenomena, and in finding applications in public opinion monitoring, disease control, transportation system design, calling center services, information recommendation. In this paper,we study the impact of human activity patterns on information diffusion. Using SIR propagation model and empirical data, conduct quantitative research on the impact of user behavior on information dissemination. It is found that when the exponent is small, user behavioral characteristics have features of many new dissemination nodes, fast information dissemination, but information continued propagation time is short, with limited influence; when the exponent is big, there are fewer new dissemination nodes, but will expand the scope of information dissemination and extend information dissemination duration; it is also found that for group behaviors, the power-law characteristic a greater impact on the speed of information dissemination than individual behaviors. This study provides a reference to better understand influence of social networking user behavior characteristics on information dissemination and kinetic effect.

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