Influence analysis of information erupted on social networks based on SIR model

2015 ◽  
Vol 26 (02) ◽  
pp. 1550018 ◽  
Author(s):  
Xue Zhou ◽  
Yong Hu ◽  
Yue Wu ◽  
Xi Xiong
Keyword(s):  
Social Networks ◽  
Relaxation Time ◽  
Social Network ◽  
Complex Networks ◽  
Evolution Equations ◽  
Dynamical Evolution ◽  
Sir Model ◽  
Peak Time ◽  
Information Propagation ◽  
Chain Reaction

In this paper, according to the similarity of chain reaction principle and the characteristics of information propagation on social network, we proposed a new word "information bomb". Based on the complex networks and SIR model, dynamical evolution equations were setup. Then methods used to evaluate the four indexes of bomb power were given, including influence breadth, influence strength, peak time and relaxation time. At last, the power of information was ascertained through these indexes. The process of information propagation is simulated to illustrate the spreading characteristics through the results. Then parameters which impact on the power of information bomb are analyzed and some methods which control the propagation of information are given.

scholarly journals Detecting the Influencer on Social Networks Using Passion Point and Measures of Information Propagation †

2020 ◽  
Vol 12 (7) ◽  
pp. 3064 ◽  
Author(s):  
Tai Huynh ◽  
Hien Nguyen ◽  
Ivan Zelinka ◽  
Dac Dinh ◽  
Xuan Hau Pham
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Marketing Strategy ◽  
Online Social Network ◽  
Modern Method ◽  
Information Propagation ◽  
The Social ◽  
User Data ◽  
The Impact ◽  
Positive Results

Influencer marketing is a modern method that uses influential users to approach goal customers easily and quickly. An online social network is a useful platform to detect the most effective influencer for a brand. Thus, we have an issue: how can we extract user data to determine an influencer? In this paper, a model for representing a social network based on users, tags, and the relationships among them, called the SNet model, is presented. A graph-based approach for computing the impact of users and the speed of information propagation, and measuring the favorite brand of a user and sharing the similar brand characteristics, called a passion point, is proposed. Therefore, we consider two main influential measures, including the extent of the influence on other people by the relationships between users and the concern to user’s tags, and the tag propagation through social pulse on the social network. Based on these, the problem of determining the influencer of a specific brand on a social network is solved. The results of this method are used to run the influencer marketing strategy in practice and have obtained positive results.

Crowd attraction-driven community evolution on social network

2021 ◽  
pp. 2250009
Author(s):  
Fuzhong Nian ◽  
Li Luo ◽  
Xuelong Yu
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Social Network ◽  
Large Scale ◽  
Detection Algorithm ◽  
Information Propagation ◽  
Evolution Analysis ◽  
Dynamic Social Networks ◽  
Community Evolution ◽  
Community Detection Algorithm

The evolution analysis of community structure of social network will help us understand the composition of social organizations and the evolution of society better. In order to discover the community structure and the regularity of community evolution in large-scale social networks, this paper analyzes the formation process and influencing factors of communities, and proposes a community evolution analysis method of crowd attraction driven. This method uses the traditional community division method to divide the basic community, and introduces the theory of information propagation into complex network to simulate the information propagation of dynamic social networks. Then defines seed node, the activity of basic community and crowd attraction to research the influence of groups on individuals in social networks. Finally, making basic communities as fixed groups in the network and proposing community detection algorithm based on crowd attraction. Experimental results show that the scheme can effectively detect and identify the community structure in large-scale social networks.

Building Complex Network Similar to Facebook

2014 ◽  
Vol 513-517 ◽  
pp. 909-913
Author(s):  
Dong Wei Guo ◽  
Xiang Yan Meng ◽  
Cai Fang Hou
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Complex Networks ◽  
Complex Network ◽  
Network Model ◽  
Clustering Coefficient ◽  
Network Characteristics ◽  
Modeling Methods ◽  
Community Strength ◽  
Strength Based

Social networks have been developed rapidly, especially for Facebook which is very popular with 10 billion users. It is a considerable significant job to build complex network similar to Facebook. There are many modeling methods of complex networks but which cant describe characteristics similar to Facebook. This paper provide a building method of complex networks with tunable clustering coefficient and community strength based on BA network model to imitate Facebook. The strategies of edge adding based on link-via-triangular, link-via-BA and link-via-type are used to build a complex network with tunable clustering coefficient and community strength. Under different parameters, statistical properties of the complex network model are analyzed. The differences and similarities are studied among complex network model proposed by this paper and real social network on Facebook. It is found that the network characteristics of the network model and real social network on Facebook are similar under some specific parameters. It is proved that the building method of complex networks is feasible.

scholarly journals A trust model for spreading gossip in social networks: a multi-type bootstrap percolation model

2020 ◽  
Vol 476 (2235) ◽  
pp. 20190826 ◽  
Author(s):  
Rinni Bhansali ◽  
Laura P. Schaposnik
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Trust Model ◽  
Percolation Model ◽  
Marketing Strategies ◽  
Bootstrap Percolation ◽  
Information Propagation ◽  
Fake News ◽  
Minimum Threshold ◽  
Bp Model

We introduce here a multi-type bootstrap percolation model, which we call T -Bootstrap Percolation ( T -BP), and apply it to study information propagation in social networks. In this model, a social network is represented by a graph G whose vertices have different labels corresponding to the type of role the person plays in the network (e.g. a student, an educator etc.). Once an initial set of vertices of G is randomly selected to be carrying a gossip (e.g. to be infected), the gossip propagates to a new vertex provided it is transmitted by a minimum threshold of vertices with different labels. By considering random graphs, which have been shown to closely represent social networks, we study different properties of the T -BP model through numerical simulations, and describe its implications when applied to rumour spread, fake news and marketing strategies.

scholarly journals Determining Community Structure and Modularity in Social Network using Genetic Algorithm

2020 ◽  
Vol 14 (3) ◽  
pp. 219
Author(s):  
Taufan Bagus Dwi Putra Aditama ◽  
Azhari SN
Keyword(s):  
Genetic Algorithm ◽  
Social Networks ◽  
Community Structure ◽  
Social Network ◽  
Complex Networks ◽  
Bottlenose Dolphins ◽  
College Football ◽  
New Algorithms

 Research on determining community structure in complex networks has attracted a lot of attention in various applications, such as email networks and social networks. The popularity determines the structure of a community because it can analyze the structure.Meanwhile, to determine the structure of the community by maximizing the value of modularity is difficult. Therefore, a lot of research introduces new algorithms to solve problems in determining community structure and maximizing the value of modularity. Genetic Algorithm can provide effective solutions by combining exploration and exploitation.This study focuses on the Genetic Algorithm which added a cleanup feature in the process. The final results of this study are the results of a comparison of modularity values based on the determination of the community structure of the Genetic Algorithm, Girvan and Newman Algorithm, and the Louvain Algorithm. The best modularity values were obtained using the Genetic Algorithm which obtained 0.6833 results for Zachary's karate club dataset, 0.7446 for the Bottlenose dolphins dataset, 0.7242 for the American college football dataset, and 0.5892 for the Books about US politics dataset.

scholarly journals ADVO: A System to Manage Influencer Marketing Campaigns on Social Networks

2021 ◽  
Vol 11 (14) ◽  
pp. 6497
Author(s):  
Tai Huynh ◽  
Hien D. Nguyen ◽  
Ivan Zelinka ◽  
Kha V. Nguyen ◽  
Vuong T. Pham ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Real Time ◽  
Management System ◽  
Digital Marketing ◽  
Information Propagation ◽  
Content Creation ◽  
Marketing Campaign ◽  
Commercial Activities ◽  
Technology Revolution

In the fourth technology revolution, influencer marketing is an essential kind of digital marketing. This marketing uses identified influencers to viral the information of products to target customers. It is useful to support brands exposed to more valuable online consumers. The influencer marketing campaign needs a management system to manage on a social network. This system helps to increase the efficiency of a campaign. This paper proposes a management system for the influencer marketing campaign, called the ADVO system. This system provides a tool for collecting data on a social network and detecting potential brand influencers for the marketing campaign. The meaningful measures for users include amplification factors for evaluating the information propagation, the passion point to measure a user’s favorite when it comes to a brand, and the content creation score for determining the ability of post-content creating. The ADVO system helps the brand to make the decision through real-time visual reports of the campaign. It is a foundation to create commercial activities and construct an advocate community of the related brand.

scholarly journals Verification of the accuracy of the SIR model in forecasting based on the improved SIR model with a constant ratio of recovery to infection rate by comparing with monitored second wave data

2021 ◽  
Vol 8 (9) ◽  
pp. 211379
Author(s):  
M. Kröger ◽  
R. Schlickeiser
Keyword(s):  
Temporal Evolution ◽  
Initial Conditions ◽  
Dynamical Evolution ◽  
Sir Model ◽  
Peak Time ◽  
Present Contribution ◽  
Analytic Expressions ◽  
Early Peak ◽  
Moderate Change ◽  
Second Wave

The temporal evolution of second and subsequent waves of epidemics such as Covid-19 is investigated. Analytic expressions for the peak time and asymptotic behaviours, early doubling time, late half decay time, and a half-early peak law, characterizing the dynamical evolution of number of cases and fatalities, are derived, where the pandemic evolution exhibiting multiple waves is described by the semi-time SIR model. The asymmetry of the epidemic wave and its exponential tail are affected by the initial conditions, a feature that has no analogue in the all-time SIR model. Our analysis reveals that the immunity is very strongly increasing in several countries during the second Covid-19 wave. Wave-specific SIR parameters describing infection and recovery rates we find to behave in a similar fashion. Still, an apparently moderate change of their ratio can have significant consequences. As we show, the probability of an additional wave is however low in several countries due to the fraction of immune inhabitants at the end of the second wave, irrespective of the ongoing vaccination efforts. We compare with alternate approaches and data available at the time of submission. Most recent data serves to demonstrate the successful forecast and high accuracy of the SIR model in predicting the evolution of pandemic outbreaks as long as the assumption underlying our analysis, an unchanged situation of the distribution of variants of concern and the fatality fraction, do not change dramatically during a wave. With the rise of the α variant at the time of submission the second wave did not terminate in some countries, giving rise to a superposition of waves that is not treated by the present contribution.

scholarly journals Multiscale mixing patterns in networks

2018 ◽  
Vol 115 (16) ◽  
pp. 4057-4062 ◽  
Author(s):  
Leto Peel ◽  
Jean-Charles Delvenne ◽  
Renaud Lambiotte
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Complex Networks ◽  
Real World ◽  
Multiple Scales ◽  
Average Level ◽  
Cultural Norms ◽  
Global Measure ◽  
Mixing Patterns ◽  
Shed Light

Assortative mixing in networks is the tendency for nodes with the same attributes, or metadata, to link to each other. It is a property often found in social networks, manifesting as a higher tendency of links occurring between people of the same age, race, or political belief. Quantifying the level of assortativity or disassortativity (the preference of linking to nodes with different attributes) can shed light on the organization of complex networks. It is common practice to measure the level of assortativity according to the assortativity coefficient, or modularity in the case of categorical metadata. This global value is the average level of assortativity across the network and may not be a representative statistic when mixing patterns are heterogeneous. For example, a social network spanning the globe may exhibit local differences in mixing patterns as a consequence of differences in cultural norms. Here, we introduce an approach to localize this global measure so that we can describe the assortativity, across multiple scales, at the node level. Consequently, we are able to capture and qualitatively evaluate the distribution of mixing patterns in the network. We find that, for many real-world networks, the distribution of assortativity is skewed, overdispersed, and multimodal. Our method provides a clearer lens through which we can more closely examine mixing patterns in networks.

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