scholarly journals Modeling of Information Diffusion in Twitter-Like Social Networks under Information Overload

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Pei Li ◽  
Wei Li ◽  
Hui Wang ◽  
Xin Zhang
Keyword(s):  
Social Networks ◽  
Information Processing ◽  
Theoretical Analysis ◽  
Information Diffusion ◽  
Information Overload ◽  
Analysis Framework ◽  
User Behaviors ◽  
Diffusion Dynamics ◽  
Different Types ◽  
Simulation Results

Due to the existence of information overload in social networks, it becomes increasingly difficult for users to find useful information according to their interests. This paper takes Twitter-like social networks into account and proposes models to characterize the process of information diffusion under information overload. Users are classified into different types according to their in-degrees and out-degrees, and user behaviors are generalized into two categories: generating and forwarding. View scope is introduced to model the user information-processing capability under information overload, and the average number of times a message appears in view scopes after it is generated by a given type user is adopted to characterize the information diffusion efficiency, which is calculated theoretically. To verify the accuracy of theoretical analysis results, we conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly. These results are of importance to understand the diffusion dynamics in social networks, and this analysis framework can be extended to consider more realistic situations.

INFORMATION DIFFUSION IN FACEBOOK-LIKE SOCIAL NETWORKS UNDER INFORMATION OVERLOAD

2013 ◽  
Vol 24 (07) ◽  
pp. 1350047 ◽  
Author(s):  
PEI LI ◽  
KAI XING ◽  
DAPENG WANG ◽  
XIN ZHANG ◽  
HUI WANG
Keyword(s):  
Social Networks ◽  
Information Processing ◽  
Theoretical Analysis ◽  
Network Structure ◽  
Information Diffusion ◽  
Information Overload ◽  
Surprising Result ◽  
Processing Capability ◽  
Simulation Results

Research on social networks has received remarkable attention, since many people use social networks to broadcast information and stay connected with their friends. However, due to the information overload in social networks, it becomes increasingly difficult for users to find useful information. This paper takes Facebook-like social networks into account, and models the process of information diffusion under information overload. The term view scope is introduced to model the user information-processing capability under information overload, and the average number of times a message appears in view scopes after it is generated is proposed to characterize the information diffusion efficiency. Through theoretical analysis, we find that factors such as network structure and view scope number have no impact on the information diffusion efficiency, which is a surprising result. To verify the results, we conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly.

ESTIMATING USER INFLUENCE IN ONLINE SOCIAL NETWORKS SUBJECT TO INFORMATION OVERLOAD

2014 ◽  
Vol 28 (03) ◽  
pp. 1450004 ◽  
Author(s):  
PEI LI ◽  
YUNCHUAN SUN ◽  
YINGWEN CHEN ◽  
ZHI TIAN
Keyword(s):  
Social Networks ◽  
Information Processing ◽  
Theoretical Analysis ◽  
Online Social Networks ◽  
Information Overload ◽  
Network Size ◽  
Finite Buffer ◽  
User Influence ◽  
Diffusion Dynamics ◽  
The Impact

Online social networks have attracted remarkable attention since they provide various approaches for hundreds of millions of people to stay connected with their friends. Due to the existence of information overload, the research on diffusion dynamics in epidemiology cannot be adopted directly to that in online social networks. In this paper, we consider diffusion dynamics in online social networks subject to information overload, and model the information-processing process of a user by a queue with a batch arrival and a finite buffer. We use the average number of times a message is processed after it is generated by a given user to characterize the user influence, which is then estimated through theoretical analysis for a given network. We validate the accuracy of our estimation by simulations, and apply the results to study the impacts of different factors on the user influence. Among the observations, we find that the impact of network size on the user influence is marginal while the user influence decreases with assortativity due to information overload, which is particularly interesting.

Information diffusion in structured online social networks

2015 ◽  
Vol 29 (13) ◽  
pp. 1550063 ◽  
Author(s):  
Pei Li ◽  
Yini Zhang ◽  
Fengcai Qiao ◽  
Hui Wang
Keyword(s):  
Social Networks ◽  
Theoretical Analysis ◽  
Online Social Networks ◽  
Information Diffusion ◽  
Word Of Mouth ◽  
Viral Marketing ◽  
User Influence ◽  
Diffusion Dynamics ◽  
Approach Infinity ◽  
Simulation Results

Nowadays, due to the word-of-mouth effect, online social networks have been considered to be efficient approaches to conduct viral marketing, which makes it of great importance to understand the diffusion dynamics in online social networks. However, most research on diffusion dynamics in epidemiology and existing social networks cannot be applied directly to characterize online social networks. In this paper, we propose models to characterize the information diffusion in structured online social networks with push-based forwarding mechanism. We introduce the term user influence to characterize the average number of times that messages are browsed which is incurred by a given type user generating a message, and study the diffusion threshold, above which the user influence of generating a message will approach infinity. We conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly. These results are of use in understanding the diffusion dynamics in online social networks and also critical for advertisers in viral marketing who want to estimate the user influence before posting an advertisement.

scholarly journals Subgraphs of Interest Social Networks for Diffusion Dynamics Prediction

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 492
Author(s):  
Valentina Y. Guleva ◽  
Polina O. Andreeva ◽  
Danila A. Vaganov
Keyword(s):  
Social Networks ◽  
Sampling Method ◽  
Building Blocks ◽  
Global Dynamics ◽  
Diffusion Dynamics ◽  
Different Types ◽  
Prediction And Control ◽  
Path Lengths ◽  
And Control ◽  
Diffusion Prediction

Finding the building blocks of real-world networks contributes to the understanding of their formation process and related dynamical processes, which is related to prediction and control tasks. We explore different types of social networks, demonstrating high structural variability, and aim to extract and see their minimal building blocks, which are able to reproduce supergraph structural and dynamical properties, so as to be appropriate for diffusion prediction for the whole graph on the base of its small subgraph. For this purpose, we determine topological and functional formal criteria and explore sampling techniques. Using the method that provides the best correspondence to both criteria, we explore the building blocks of interest networks. The best sampling method allows one to extract subgraphs of optimal 30 nodes, which reproduce path lengths, clustering, and degree particularities of an initial graph. The extracted subgraphs are different for the considered interest networks, and provide interesting material for the global dynamics exploration on the mesoscale base.

Susceptible-Infectious-Critical-Recovered rumor spreading model in social networks

2021 ◽  
Author(s):  
Wang Hongmei ◽  
Qiu Liqing ◽  
Tan Kun ◽  
Cui Junwei
Keyword(s):  
Social Networks ◽  
Theoretical Analysis ◽  
Critical State ◽  
Effective Measure ◽  
Rumor Spreading ◽  
Spreading Model ◽  
Rumor Propagation ◽  
Number Formula ◽  
Simulation Results ◽  
Next Generation Matrix

As an important area of social networks, rumor spread has attracted the attention of many scholars. It aims to explore the rumor propagation, and to propose effective measures to curb the further spread of rumors. Different from some existing works, this paper believes that susceptible persons affected by rumor-refuting information will first enter the critical state, while ones who related to rumors will directly turn into the spread state. Therefore, this paper proposes a Susceptible-Infectious-Critical-Recovered (SICR) rumor model. In addition, considering that infectious persons with high levels of refuting rumors may cause emotional resonance among individuals, this model adds a connecting edge from the recovered to the infectious who are triggered by the information of refuting the rumors. First, the basic regeneration number [Formula: see text] is obtained by using the next generation matrix method. Then, the global stability of the rumor-free equilibrium [Formula: see text] and the persistence of rumor propagation are proved in detail in theoretical analysis. The simulation results show that the existence of a critical state can reduce the influence of rumors. Rumor refutation mechanism, as soon as possible to curb the spread of rumors, is an effective measure.

scholarly journals Quantum Probabilistic Models Using Feynman Diagram Rules for Better Understanding the Information Diffusion Dynamics in Online Social Networks

2020 ◽  
Vol 34 (10) ◽  
pp. 13730-13731
Author(s):  
Ece C. Mutlu
Keyword(s):  
Social Networks ◽  
Social Learning ◽  
Complex Systems ◽  
Bayesian Networks ◽  
Online Social Networks ◽  
Information Diffusion ◽  
Probabilistic Models ◽  
Human Interaction ◽  
Multi Agent Systems ◽  
Diffusion Dynamics

This doctoral consortium presents an overview of my anticipated PhD dissertation which focuses on employing quantum Bayesian networks for social learning. The project, mainly, aims to expand the use of current quantum probabilistic models in human decision-making from two agents to multi-agent systems. First, I cultivate the classical Bayesian networks which are used to understand information diffusion through human interaction on online social networks (OSNs) by taking into account the relevance of multitude of social, psychological, behavioral and cognitive factors influencing the process of information transmission. Since quantum like models require quantum probability amplitudes, the complexity will be exponentially increased with increasing uncertainty in the complex system. Therefore, the research will be followed by a study on optimization of heuristics. Here, I suggest to use an belief entropy based heuristic approach. This research is an interdisciplinary research which is related with the branches of complex systems, quantum physics, network science, information theory, cognitive science and mathematics. Therefore, findings can contribute significantly to the areas related mainly with social learning behavior of people, and also to the aforementioned branches of complex systems. In addition, understanding the interactions in complex systems might be more viable via the findings of this research since probabilistic approaches are not only used for predictive purposes but also for explanatory aims.

scholarly journals The Great Chains of Computing: Informatics at Multiple Scales

2011 ◽  
Vol 9 (2) ◽  
pp. 434-443
Author(s):  
Kevin Kirby ◽  
James Walden ◽  
Rudy Garns ◽  
Maureen Doyle
Keyword(s):  
Social Networks ◽  
Information Processing ◽  
Multiple Scales ◽  
Quantum Level ◽  
Western Thought ◽  
Different Types ◽  
Great Chain Of Being ◽  
Characteristic Features ◽  
The Universe

The perspective from which information processing is pervasive in the universe has proven to be an increasingly productive one. Phenomena from the quantum level to social networks have commonalities that can be usefully explicated using principles of informatics. We argue that the notion of scale is particularly salient here. An appreciation of what is invariant and what is emergent across scales, and of the variety of different types of scales, establishes a useful foundation for the transdiscipline of informatics. We survey the notion of scale and use it to explore the characteristic features of information statics (data), kinematics (communication), and dynamics (processing). We then explore the analogy to the principles of plenitude and continuity that feature in Western thought, under the name of the "great chain of being", from Plato through Leibniz and beyond, and show that the pancomputational turn is a modern counterpart of this ruling idea. We conclude by arguing that this broader perspective can enhance informatics pedagogy.

scholarly journals The Great Chains of Computing: Informatics at Multiple Scales

2011 ◽  
Vol 9 (2) ◽  
pp. 434-443
Author(s):  
Kevin Kirby ◽  
James Walden ◽  
Rudy Garns ◽  
Maureen Doyle
Keyword(s):  
Social Networks ◽  
Information Processing ◽  
Multiple Scales ◽  
Quantum Level ◽  
Western Thought ◽  
Different Types ◽  
Great Chain Of Being ◽  
Characteristic Features ◽  
The Universe

The perspective from which information processing is pervasive in the universe has proven to be an increasingly productive one. Phenomena from the quantum level to social networks have commonalities that can be usefully explicated using principles of informatics. We argue that the notion of scale is particularly salient here. An appreciation of what is invariant and what is emergent across scales, and of the variety of different types of scales, establishes a useful foundation for the transdiscipline of informatics. We survey the notion of scale and use it to explore the characteristic features of information statics (data), kinematics (communication), and dynamics (processing). We then explore the analogy to the principles of plenitude and continuity that feature in Western thought, under the name of the "great chain of being", from Plato through Leibniz and beyond, and show that the pancomputational turn is a modern counterpart of this ruling idea. We conclude by arguing that this broader perspective can enhance informatics pedagogy.

scholarly journals Capturing Dynamics of Information Diffusion in SNS: A Survey of Methodology and Techniques

2023 ◽  
Vol 55 (1) ◽  
pp. 1-51
Author(s):  
Huacheng Li ◽  
Chunhe Xia ◽  
Tianbo Wang ◽  
Sheng Wen ◽  
Chao Chen ◽  
...  
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Source Identification ◽  
Information Diffusion ◽  
Viral Marketing ◽  
Diffusion Modeling ◽  
Diffusion Dynamics ◽  
Information User ◽  
Pros And Cons ◽  
Open Issues

Studying information diffusion in SNS (Social Networks Service) has remarkable significance in both academia and industry. Theoretically, it boosts the development of other subjects such as statistics, sociology, and data mining. Practically, diffusion modeling provides fundamental support for many downstream applications (e.g., public opinion monitoring, rumor source identification, and viral marketing). Tremendous efforts have been devoted to this area to understand and quantify information diffusion dynamics. This survey investigates and summarizes the emerging distinguished works in diffusion modeling. We first put forward a unified information diffusion concept in terms of three components: information, user decision, and social vectors, followed by a detailed introduction of the methodologies for diffusion modeling. And then, a new taxonomy adopting hybrid philosophy (i.e., granularity and techniques) is proposed, and we made a series of comparative studies on elementary diffusion models under our taxonomy from the aspects of assumptions, methods, and pros and cons. We further summarized representative diffusion modeling in special scenarios and significant downstream tasks based on these elementary models. Finally, open issues in this field following the methodology of diffusion modeling are discussed.

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