Social Network Information Propagation Model Based on Individual Behavior

In this paper, we discuss building an information dissemination model based on individual behavior. We analyze the individual behavior related to information dissemination and the factors that affect the sharing behavior of individuals, and we define and quantify these factors. We consider these factors as characteristic attributes and use a Bayesian classifier to classify individuals. Considering the forwarding delay characteristics of information dissemination, we present a random time generation method that simulates the delay of information dissemination. Given time and other constraints, a user might not look at all the information that his/her friends published. Therefore, this paper proposes an algorithm to predict information visibility, i.e., it estimates the probability that an individual will see the information. Based on the classification of individual behavior and combined with our random time generation and information visibility prediction method, we propose an information dissemination model based on individual behavior. The model can be used to predict the scale and speed of information propagation. We use data sets from Sina Weibo to validate and analyze the prediction methods of the individual behavior and information dissemination model based on individual behavior. A previously proposedinformation dissemination model provides the foundation for a subsequent study on the evolution of the network and social network analysis. Predicting the scale and speed of information dissemination can also be used for public opinion monitoring.

Research on E-mail Communication Network Evolution Model Based on User Information Propagation

E-mail communication network evolution model based on user information propagation is studied. First, mathematical representation of weighted e-mail communication network is proposed, and network center parameters of Enron dataset and the distribution of node degree and strength are analyzed. Then, some rules of e-mail communication network evolution are found. Second, the model of e-mail information propagation is described, and e-mail communication network evolution model based on user information propagation is proposed. Lastly, the simulation proves the correctness of the distribution characteristic of degree and strength of the model proposed and then verifies that the model proposed is closer to the real situation of e-mail communication network through parameter comparison. This research provides the basis for other researches on social network evolution and data communication.

Impact of Online Community Structure on Information Propagation:Empirical Analysis and Modeling

Online social networking sites ( OSNS) ,as a popular social media platform,have been developed massively for business and research purposes. In this paper,it investigated the impact of community structure in online social network on information propagation. A SI (Susceptible-Infected) model based on community structure was proposed. In the SI model,the heterogeneity of user's active time was taken into account. From the results,it was found that the number of links among communities determines the fraction of infected nodes. With the increase of the number of groups G,however,the fraction of infected nodes remains approximately constant. The simulation results will be of great significance: the information will last relatively short for group networks which have either a small or a large number of groups. The results can be useful for optimizing or controlling information,such as propagating rumors in online social networks.

The effect of propagation delay on the dynamic evolution of the Bitcoin blockchain

This paper analyzes the selfish-mine strategy in the Bitcoin blockchain introduced in 2013 by I.Eyal and E.G.Sirer.This strategy could be used by a colluding pool of miners to earn more than their fair share of the mining revenue and in consequence to force other honest miners to join them to decrease the variance of their revenues and make their monthly revenues more predictable.It is a very dangerous dynamic that could allow the rogue pool of miners to go toward a majority by accumulating powers of news adherents and control the entire network.Considering that the propagation delay of information between any two miners in the network,which is not negligible and follows a normal distribution with mean proportional to the physical distance between the two miners,and a constant variance independent of others'delays,we prove that no guarantee can be given about the success or failure of the selfish-mine attack because of the variability of information propagation in the network.

Research on Influencing Factors of Information Dissemination Based on User Preference

The rapid development of the Intemet makes the social network of information dissemination has undergone tremendous changes. Based on the introduction of social network information dissemination mode, this paper analyzes the influencing factors of information dissemination, establishes the user preference model through CP-nets tool, and combines the AHP principle to mine the user＇s preference order, and obtain the user＇s optimal preference feature Portfolio, and finally collect the user in the microblogging platform in the historical behavior data. the use of NetLogo different users of information dissemination decision to predict.

Time sequential influence maximization algorithm based on neighbor node influence

In view of the forwarding microblogging,secondhand smoke,happiness,and many other phenomena in real life,the spread characteristic of the secondary neighbor nodes in this kind of phenomenon and network scheduling is extracted,and sequence influence maximization problem based on the influence of neighbor nodes is proposed in this paper.That is,in the time sequential social network,the propagation characteristics of the second-level neighbor nodes are considered emphatically,and k nodes are found to maximize the information propagation.Firstly,the propagation probability between nodes is calculated by the improved degree estimation algorithm.Secondly,the weighted cascade model(WCM) based on static social network is not suitable for temporal social network.Therefore,an improved weighted cascade model(IWCM) is proposed,and a second-level neighbors time sequential maximizing influence algorithm(STIM) is put forward based on node degree.It combines the consideration of neighbor nodes and the problem of overlap of influence scope between nodes,and makes it chronological.Finally,the experiment verifies that STIM algorithm has stronger practicability,superiority in influence range and running time compared with similar algorithms,and is able to solve the problem of maximizing the timing influence based on the influence of neighbor nodes.

Understanding the mechanism of social tie in the propagation process of social network with communication channel

The propagation of information in online social networks plays a critical role in modern life,and thus has been studied broadly.Researchers have proposed a series of propagation models,generally,which use a single transition probability or consider factors such as content and time to describe the way how a user activates her/his neighbors.However,the research on the mechanism how social ties between users play roles in propagation process is still limited.Specifically,comprehensive summary of factors which affect user’s decision whether to share neighbor’s content was lacked in existing works,so that the existing models failed to clearly describe the process a user be activated by a neighbor.To this end,in this paper,we analyze the close correspondence between social tie in propagation process and communication channel,thus we propose to exploit the communication channel to describe the information propagation process between users,and design a social tie channel(STC)model.The model can naturally incorporate many factors affecting the information propagation through edges such as content topic and user preference,and thus can effectively capture the user behavior and relationship characteristics which indicate the property of a social tie.Extensive experiments conducted on two real-world datasets demonstrate the effectiveness of our model on content sharing prediction between users.

Security and performance evaluation of master node protocol based reputation blockchain in the bitcoin network

Bitcoin is a digital currency based on a peer-to-peer network to propagate and verify transactions.Bitcoin is gaining wider adoption than any previous crypto-currency.However,the mechanism of peers randomly choosing logical neighbours without any knowledge about the underlying physical topology can cause a delay overhead in information propagation which makes the system vulnerable to double spend attacks.Aiming at alleviating the propagation delay problem,this paper introduces a proximity-aware extension to the current Bitcoin protocol,named Master Node Based Clustering(MNBC).The ultimate purpose of the proposed protocol,which is based on how clusters are formulated and how nodes can define their membership,is to improve the information propagation delay in the Bitcoin network.In the MNBC protocol,physical internet connectivity increases as well as the number of hops between nodes decreases through assigning nodes to be responsible for maintaining clusters based on physical Internet proximity.Furthermore,a reputation-based blockchain protocol is integrated with MNBC protocol in order to securely assign a master node for every cluster.We validate our proposed methods through a set of simulation experiments and the findings show how the proposed methods run and their impact in optimising the transaction propagation delay.