Exponentially time decaying susceptible-informed (SIT) model for information diffusion process on networks

2018 ◽  
Vol 28 (6) ◽  
pp. 063129
Author(s):  
Wei Bao ◽  
George Michailidis
Keyword(s):  
Diffusion Process ◽  
Information Diffusion ◽  
Sit Model

scholarly journals Influential Performance of Nodes Identified by Relative Entropy in Dynamic Networks

2020 ◽  
Vol 08 (01) ◽  
pp. 93-112
Author(s):  
Péter Marjai ◽  
Attila Kiss
Keyword(s):  
Diffusion Process ◽  
Complex Networks ◽  
Relative Entropy ◽  
Information Diffusion ◽  
Dynamic Networks ◽  
Centrality Measures ◽  
Topsis Method ◽  
Cover Time ◽  
Influential Nodes ◽  
Different Characteristics

For decades, centrality has been one of the most studied concepts in the case of complex networks. It addresses the problem of identification of the most influential nodes in the network. Despite the large number of the proposed methods for measuring centrality, each method takes different characteristics of the networks into account while identifying the “vital” nodes, and for the same reason, each has its advantages and drawbacks. To resolve this problem, the TOPSIS method combined with relative entropy can be used. Several of the already existing centrality measures have been developed to be effective in the case of static networks, however, there is an ever-increasing interest to determine crucial nodes in dynamic networks. In this paper, we are investigating the performance of a new method that identifies influential nodes based on relative entropy, in the case of dynamic networks. To classify the effectiveness, the Suspected-Infected model is used as an information diffusion process. We are investigating the average infection capacity of ranked nodes, the Time-Constrained Coverage as well as the Cover Time.

scholarly journals An epidemic model for correlated information diffusion in crowd intelligence networks

2019 ◽  
Vol 3 (2) ◽  
pp. 168-183 ◽  
Author(s):  
Yuejiang Li ◽  
H. Vicky Zhao ◽  
Yan Chen
Keyword(s):  
Diffusion Process ◽  
Mixture Model ◽  
Information Diffusion ◽  
Stable State ◽  
Stable Condition ◽  
Real Data ◽  
Information Flows ◽  
Content Type ◽  
Correlated Information ◽  
Crowd Intelligence

Purpose With the popularity of the internet and the increasing numbers of netizens, tremendous information flows are generated daily by the intelligently interconnected individuals. The diffusion processes of different information are not independent, and they interact with and influence each other. Modeling and analyzing the interaction between correlated information play an important role in the understanding of the characteristics of information dissemination and better control of the information flows. This paper aims to model the correlated information diffusion process over the crowd intelligence networks. Design/methodology/approach This study extends the classic epidemic susceptible–infectious–recovered (SIR) model and proposes the SIR mixture model to describe the diffusion process of two correlated pieces of information. The whole crowd is divided into different groups with respect to their forwarding state of the correlated information, and the transition rate between different groups shows the property of each piece of information and the influences between them. Findings The stable state of the SIR mixture model is analyzed through the linearization of the model, and the stable condition can be obtained. Real data are used to validate the SIR mixture model, and the detailed diffusion process of correlated information can be inferred by the analysis of the parameters learned through fitting the real data into the SIR mixture model. Originality/value The proposed SIR mixture model can be used to model the diffusion of correlated information and analyze the propagation process.

scholarly journals LEARNING FROM THE EXPECTATIONS OF OTHERS

2008 ◽  
Vol 12 (3) ◽  
pp. 345-377 ◽  
Author(s):  
JIM GRANATO ◽  
ERAN A. GUSE ◽  
M. C. SUNNY WONG
Keyword(s):  
Asymmetric Information ◽  
Diffusion Process ◽  
Adaptive Learning ◽  
Information Diffusion ◽  
Multiple Equilibria ◽  
Forecast Error ◽  
Mean Square ◽  
Cobweb Model ◽  
Boomerang Effect ◽  
Forecast Efficiency

This paper explores the equilibrium properties of boundedly rational heterogeneous agents under adaptive learning. In a modified cobweb model with a Stackelberg framework, there is an asymmetric information diffusion process from leading to following firms. It turns out that the conditions for at least one learnable equilibrium are similar to those under homogeneous expectations. However, the introduction of information diffusion leads to the possibility of multiple equilibria and can expand the parameter space of potential learnable equilibria. In addition, the inability to correctly interpret expectations will cause a “boomerang effect” on the forecasts and forecast efficiency of the leading firms. The leading firms' mean square forecast error can be larger than that of following firms if the proportion of following firms is sufficiently large.

How multiple social networks affect user awareness: The information diffusion process in multiplex networks

2015 ◽  
Vol 92 (4) ◽  
Author(s):  
Weihua Li ◽  
Shaoting Tang ◽  
Wenyi Fang ◽  
Quantong Guo ◽  
Xiao Zhang ◽  
...  
Keyword(s):  
Social Networks ◽  
Diffusion Process ◽  
Information Diffusion ◽  
Multiplex Networks ◽  
User Awareness

Space, time, and disease on social media: a case study of dengue fever in China

GEOMATICA ◽  
2018 ◽  
Vol 72 (4) ◽  
pp. 112-126
Author(s):  
Junfang Gong ◽  
Shengwen Li ◽  
Jay Lee
Keyword(s):  
Social Media ◽  
Diffusion Process ◽  
Information Diffusion ◽  
Standard Form ◽  
Spatial Relationship ◽  
Spatiotemporal Patterns ◽  
Spatial And Temporal Patterns ◽  
Dynamic Events ◽  
Spatiotemporal Trends ◽  
Social Events

It is possible to generate real-time and location-by-location data of many types of human dynamic events based on social media information for the awareness of events in public health. Analyzing these events is useful in understanding spatiotemporal trends and patterns of how diseases spread and also provides indications for users’ sentiment about the concerned disease. This article examines the spatial and temporal patterns of social media posts based on the content, attributes, and follower activities of posts on social media. We describe the spatial features of the topic discussed in the posts and the spatial relationship among comments on the posts. We present models for describing the diffusion process of these posts and for exploring their spatiotemporal patterns. Our results suggest that (1) the long-term trends of the topics in users’ views seem to be stable, (2) results from analyzing follower activities of posts are critical in describing the spatial patterns of the posts, and (3) the diffusion process of an event in social media is still similar to that of a traditional information diffusion model. Our findings are useful for understanding social media and social events. The processes we describe in this article suggest a standard form of analysis that can be adopted for extracting spatiotemporal patterns of information diffusion and for data mining in social media posts.

scholarly journals Research on the Path of Network Opinion Expression in AI Environment for College Students

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yue Zhu ◽  
Muhammad Talha
Keyword(s):  
Diffusion Process ◽  
Information Diffusion ◽  
Topic Model ◽  
Threshold Model ◽  
Opinion Leader ◽  
Group Model ◽  
Two Stage ◽  
Model Based ◽  
Linear Threshold ◽  
Linear Threshold Model

Network interaction has evolved into a grouping paradigm as civilization has progressed and artificial intelligence technology has advanced. This network group model has quickly extended communication space, improved communication content, and tailored to the demands of netizens. The fast growth of the network community on campus can assist students in meeting a variety of communication needs and serve as a vital platform for their studies and daily lives. It is investigated how to extract opinion material from comment text. A strategy for extracting opinion attitude words and network opinion characteristic words from a single comment text is offered at a finer level. The development of a semiautonomous domain emotion dictionary generating technique improves the accuracy of opinion and attitude word extraction. This paper proposes a window-constrained Latent Dirichlet Allocation (LDA) topic model that improves the accuracy of extracting network opinion feature words and ensures that network opinion feature words and opinion attitude words are synchronized by using the location information of opinion attitude words. The two-stage opinion leader mining approach and the linear threshold model based on user roles are the subjects of model simulation tests in this study. It is demonstrated that the two-stage opinion leader mining method suggested in this study can greatly reduce the running time while properly finding opinion leaders with stronger leadership by comparing the results with existing models. It also shows that the linear threshold model based on user roles proposed in this paper can effectively limit the total number of active users who are activated multiple times during the information diffusion process by distinguishing the effects of different user roles on the information diffusion process.

Sign in / Sign up

Export Citation Format

Share Document