Virality: Developing a Rigorous and Useful Definition of an Information Diffusion Process

2011 ◽  
Author(s):  
Jeff Hemsley
Keyword(s):  
Diffusion Process ◽  
Information Diffusion ◽  
Definition Of

Efficient Time Series Clustering and Its Application to Social Network Mining

2014 ◽  
Vol 23 (2) ◽  
pp. 213-229 ◽  
Author(s):  
Cangqi Zhou ◽  
Qianchuan Zhao
Keyword(s):  
Time Series ◽  
Social Network ◽  
Information Diffusion ◽  
Time Series Data ◽  
Online Social Network ◽  
Dissimilarity Measure ◽  
Series Data ◽  
Formation Mechanisms ◽  
Network Analyses ◽  
Definition Of

AbstractMining time series data is of great significance in various areas. To efficiently find representative patterns in these data, this article focuses on the definition of a valid dissimilarity measure and the acceleration of partitioning clustering, a common group of techniques used to discover typical shapes of time series. Dissimilarity measure is a crucial component in clustering. It is required, by some particular applications, to be invariant to specific transformations. The rationale for using the angle between two time series to define a dissimilarity is analyzed. Moreover, our proposed measure satisfies the triangle inequality with specific restrictions. This property can be employed to accelerate clustering. An integrated algorithm is proposed. The experiments show that angle-based dissimilarity captures the essence of time series patterns that are invariant to amplitude scaling. In addition, the accelerated algorithm outperforms the standard one as redundancies are pruned. Our approach has been applied to discover typical patterns of information diffusion in an online social network. Analyses revealed the formation mechanisms of different patterns.

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.

The Information Entropy Prisms on Riverine Water Quality Evolution

2020 ◽  
Author(s):  
Tianrui Pang ◽  
Jiping Jiang ◽  
Bellie Sivakuamr ◽  
Yi Zheng ◽  
Tong Zheng
Keyword(s):  
Water Quality ◽  
Diffusion Process ◽  
Information Entropy ◽  
Solute Diffusion ◽  
System Boundary ◽  
Time And Space ◽  
Entropy Index ◽  
Boundary Definition ◽  
Definition Of ◽  
Quality Process

<p>Information entropy theory has been largely applied in hydrological modeling and engineering optimization. Recently the entropy description and explanation of reactive solute mixing and transport process has received increasing attentions. Literatures mainly focus theoretical analysis on hypothetical cases, however, the direct observation and calculation with field datasets are hardly reported.</p><p>This work studied the change of information entropy in surface water solute transport system with field data. A comprehensive information entropy based analysis framework were proposed, which works like a combined optical system with Optical Sources-Filters-Prisms-Images. We established four basic probability space, leading to four basic information entropy indexes: Dilution index (E), Flux index (F), Spatial entropy index (Gx) , and Temporal entropy index (Gt).</p><p>The evolution characteristic of information entropy in one-component solute diffusion system is studied by using the method of discrete information entropy analysis. In the system boundary definition of fixed observation, the information entropy appears a peak in time and space dimension, and the peak value of information entropy appears in the first 20%-30% of the fixed observation interval, while in the system boundary definition of dynamic observation, information entropy decreases continuously with the increase of time and space distance. Through the local sensitivity analysis of the hydrodynamic parameters of the above analytical solutions, it is found that the sensitivity of information entropy H to diffusion coefficient Dx is relatively constant, and the greater the degradation coefficient k is, the more sensitive the monitoring time t is to k, the more sensitive the spatial change of information entropy is to the change of flow velocity ux with the increase of distance, while the change of time is insensitive to ux.</p><p>Furthermore, the evolution characteristic of information entropy in complex water quality process of rivers is studied. The Guangming section of Maozhou River in Shenzhen is taken as the research area. BOD-DO and nitrogen elements (NH3-N, NO3-N, Org-N) water quality process were selected, and one-dimensional S-P model and WASP_EUTRO water quality model were constructed respectively. After model calibration and verification, the changing characteristics of information entropy, mutual information and information transfer index are analyzed under the system definition of fixed observation. It was found that the transformation reaction process gradually replaced the diffusion process in the complex water quality process as the main factor affecting the change of information entropy, and the information entropy change law in the single component diffusion process no longer exists in the complex water quality process.</p>

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