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In recent past, Online Social Networks (OSN) has emerged as a platform for sharing information, thoughts, and activities. In the real-world network, method of considering the appropriate samples is most frequently used for network analysis. Graph sampling is a procedure used for computing unknown parameters. Many sampling algorithms exist in literature such as Random node, Random edge sampling, Rank degree, etc. can be used for estimation. This paper presents a comparison of clique based procedure (CBP) and shortest path based procedure (SPP) to estimate the average degree of a vertex in a social network using an overlapping cluster sampling. A comparative procedure is used to obtain the lower and upper limit of confidence intervals with the help of multiple samples. Ogive based simulation is also used for single value computation of limits of CI. The results, obtained from simulation, show that clique based sampling algorithm (CBP) is more efficient than the shortest path based sampling algorithm (SPP). The estimated confidence intervals can be used for monitoring the reliability of a social network in terms of control over average network degree. [ABSTRACT FROM AUTHOR]

Mathematical modeling is an important approach to research rumor propagation in online social networks. Most of prior work about rumor propagation either carried out empirical studies or focus on ordinary differential equation models with only consideration of temporal dimension; little attempt has been given on understanding rumor propagation over both temporal and spatial dimensions. This paper primarily addresses an issue related to how to define a spatial distance in online social networks by clustering and then proposes a partial differential equation model with a time delay to describing rumor propagation over both temporal and spatial dimensions. Theoretical analysis reveals the existence of equilibrium points, a priori bound of the solution, the local stability and the global stability of equilibrium points of our rumor propagation model. Finally, numerical simulations have analyzed the possible influence factors on rumor propagation and proved the validity of the theoretical analysis. [ABSTRACT FROM AUTHOR]

In recent years, online social networks have become an important site for companies to promote their latest products. Consequently, evaluating how many clients are affected by preferential information distributed in online social networks has become essential. In this paper, a novel dynamic model called the follower super forwarder client (FSFC) model is proposed to address the spreading behavior of preferential information in online social networks. The mean field theory is adopted to describe the formulas of the FSFC model and the key parameters of the model are derived from the past forwarding data of the preferential information. The edge between a large-degree node to a small-degree node has a greater weight. In addition, two kinds of infection probabilities are adopted for large-degree node forwarders and small-degree node forwarders. To evaluate the performance of the FSFC model, preferential data published on the Sina microblog ( www.weibo.com ) for the Vivo smartphone, Alibaba’s Tmall shopping site, and the Xiaomi phone were selected as real cases. Simulation results indicate that the relative errors of the output of the FSFC model compared with the actual data are 0.0068% (Vivo smartphone), 0.0085% (Tmall), and 0.032% (Xiaomi phone), respectively. The results verify that the FSFC model is a feasible model for describing the spreading behavior of preferential information in online social networks. [ABSTRACT FROM AUTHOR]

Social networks have involved more and more users who search for and share information extensively and frequently. Tremendous evidence in Facebook, Twitter, Flickr and Google+ alike shows that such social networks are the major information sources as well as the most effective platforms for information transmission and exchange. The dynamic propagation of various information may gradually disseminate, drastically increase, strongly compete with each other, or slowly decrease. These observations had led to the present study of the spreading process of true and fake information over social networks, particularly the Facebook. Specifically, in this paper the topological structure of two huge-scale Facebook network datasets are investigated regarding their statistical properties. Based on that, an information model for simulating the true and fake information spreading over the Facebook is established. Through controlling the spreading parameters in extensive large-scale simulations, it is found that the final density of stiflers increases with the growth of the spreading rate, while it would decline with the increase of the removal rate. Moreover, it is found that the spreading process of the true–fake information is closely related to the node degrees on the network. Hub-individuals with high degrees have large probabilities to learn hidden information and then spread it. Interestingly, it is found that the spreading rate of the true information but not of the fake information has a great effect on the information spreading process, reflecting the human nature in believing and spreading truths in social activities. The new findings validate the proposed model to be capable of characterizing the dynamic evolution of true and fake information over the Facebook, useful and informative for future social science studies. [ABSTRACT FROM AUTHOR]

With the wide applications of social networks, government and individuals increasingly emphasize information networks security. This paper is devoted to investigating a reaction–diffusion malware propagation model with mixed delays to describe the process of social networks. Applying matrix theory for characteristic values, we establish the local stability conditions of a positive equilibrium point. Based on the linear approximation method of nonlinear systems, the Hopf bifurcation at the positive equilibrium point is considered. Additionally, we identify some sensitive parameters in the process of malware propagation that are significant for control theory. Finally, numerical simulations are performed to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

This paper sets up a rumor spreading model in online social networks based on the European fox rabies SIR model. The model considers the impact of changing number of online social network users, combines the transmission dynamics to set up a population dynamics of rumor spreading model in online social networks. Simulation is carried out on online social network, and results show that the new rumor spreading model is in accordance with the real propagation characteristics in online social networks. [ABSTRACT FROM AUTHOR]

Social applications play a very important role in people’s lives, as users communicate with each other through social networks on a daily basis. This presents a challenge: How does one receive high-quality service from social networks at a low cost? Users can access different kinds of wireless networks from various locations. This paper proposes a user access management strategy based on network pricing such that networks can increase its income and improve service quality. Firstly, network price is treated as an optimizing access parameter, and an unascertained membership algorithm is used to make pricing decisions. Secondly, network price is adjusted dynamically in real time according to network load. Finally, selecting a network is managed and controlled in terms of the market economy. Simulation results show that the proposed scheme can effectively balance network load, reduce network congestion, improve the user's quality of service (QoS) requirements, and increase the network’s income. [ABSTRACT FROM AUTHOR]

This paper establishes a novel Susceptible-Infected-Removed (SIR) rumor spreading model for online social networks (OSNs). The model utilizes the node degree to describe the dynamic changes of the number of rumor spreaders and it can be regarded as an extension of the traditional SIR model. Stability analysis of the model reveals that the spreader in social networks has a basic reproduction number. If the basic reproduction number is less than 1, then rumors will disappear. Otherwise, rumors will persist. According to this result, we can predict the trend of rumor spreading. Then we propose an immune-structure SIR model to explore the control method of rumor spreading. Stability analysis and numerical simulation of the model indicate that immunizing susceptible individual is an effective method to control rumors. Further, the immune-structure model explains that the network structure decides the choice of immune methods. Our findings offer some new insights to control the spread of rumors on OSNs. [ABSTRACT FROM AUTHOR]

In online social media, opinion divergences and differentiations generally exist as a result of individuals’ extensive participation and personalization. In this paper, a Susceptible–Hesitated–Infected–Removed (SHIR) model is proposed to study the dynamics of competitive dual information diffusion. The proposed model extends the classical SIR model by adding hesitators as a neutralized state of dual information competition. It is both hesitators and stable spreaders that facilitate information dissemination. Researching on the impacts of diffusion parameters, it is found that the final density of stiflers increases monotonically as infection rate increases and removal rate decreases. And the advantage information with larger stable transition rate takes control of whole influence of dual information. The density of disadvantage information spreaders slightly grows with the increase of its stable transition rate, while whole spreaders of dual information and the relaxation time remain almost unchanged. Moreover, simulations imply that the final result of competition is closely related to the ratio of stable transition rates of dual information. If the stable transition rates of dual information are nearly the same, a slightly reduction of the smaller one brings out a significant disadvantage in its propagation coverage. Additionally, the relationship of the ratio of final stiflers versus the ratio of stable transition rates presents power characteristic. [ABSTRACT FROM AUTHOR]

The propagation of memes on online social networks often depends on the mechanism of social affirmation. Centola termed such social-affirmation-driven diffusion as complex contagion and partly validated it through an online experiment. However, for actual online meme propagation, the mechanism of social affirmation often takes effect in combination with various other factors and mechanisms. In this paper, we examine the combinatorial effects of social affirmation and the attractiveness and persistence of the meme by proposing and analyzing a UACI model, where an agent's activities to receive and transfer a meme is associated with the transition between its four possible states of 'Uninformed', 'Attended','Convinced' and 'Immune'. The numerical simulations illustrate nontrivial patterns of propagation. Especially, it is revealed that the effects of simple and complex contagions co-exist and equilibrate in accordance with the joint functions of meme attractiveness and social affirmation. Furthermore, the low-persistence of the meme hinders the propagation-scale more remarkably on the regular network than on the random one, indicating that the persistence may be critical for retaining complex contagion. [ABSTRACT FROM AUTHOR]

A peer-to-peer (P2P) file sharing system provides a platform that enables a tremendous number of nodes to share their files. Retrieving desired files efficiently and trustworthily is critical in such a large and jumbled system. However, the issues of efficient searching and trustworthy searching have only been studied separately. Simply combining the methods to achieve the two goals doubles system overhead. In this paper, we first study trace data from Facebook and BitTorrent. Guided by the observations, we propose a system that integrates a social network into a P2P network, named Social-P2P, for simultaneous efficient and trustworthy file sharing. It incorporates three mechanisms: (1) interest/trust-based structure, (2) interest/trust-based file searching, and (3) trust relationship adjustment. By exploiting the social interests and relationships in the social network, the interest/trust-based structure groups common-multi-interest nodes into a cluster and further connects socially close nodes within a cluster. The comparably stable nodes in each cluster form a Distributed Hash Table (DHT) for inter-cluster file searching. In the interest/trust-based file searching mechanism, a file query is forwarded to the cluster of the file by the DHT routing first. Then, it is forwarded along constructed connections within a cluster, which achieves high hit rate and reliable routing. Moreover, sharing files among socially close friends discourages nodes from providing faulty files because people are unlikely to risk their reputation in the real-world. In the trust relationship adjustment mechanism, each node in a routing path adaptively decreases its trust on the node that has forwarded a faulty file in order to avoid routing queries towards misbehaving nodes later on. We conducted extensive trace-driven simulations and implemented a prototype on PlanetLab. Experimental results show that Social-P2P achieves highly efficient and trustworthy file sharing compared to current file sharing systems and trust management systems. [ABSTRACT FROM AUTHOR]

With the development of Internet and wireless communication, virtual social networks are becoming increasingly important in the formation of nowadays’ social communities. Topological evolution model is foundational and critical for social network related researches. Up to present most of the related research experiments are carried out on artificial networks, however, a study of incorporating the actual social activities into the network topology model is ignored. This paper first formalizes two mathematical abstract concepts of hobbies search and friend recommendation to model the social actions people exhibit. Then a social activities based topology evolution simulation model is developed to satisfy some well-known properties that have been discovered in real-world social networks. Empirical results show that the proposed topology evolution model has embraced several key network topological properties of concern, which can be envisioned as signatures of real social networks. [ABSTRACT FROM AUTHOR]

In this paper, we propose a model to investigate how truth affects rumor diffusion in online social media. Our model reveals a relation between rumor and truth - namely, when a rumor is diffusing, the truth about the rumor also diffuses with it. Two patterns of the agents used to identify rumor, self-identification and passive learning are taken into account. Combining theoretical proof and simulation analysis, we find that the threshold value of rumor diffusion is negatively correlated to the connectivity between nodes in the network and the probability β of agents knowing truth. Increasing β can reduce the maximum density of the rumor spreaders and slow down the generation speed of new rumor spreaders. On the other hand, we conclude that the best rumor diffusion strategy must balance the probability of forwarding rumor and the probability of agents losing interest in the rumor. High spread rate λ of rumor would lead to a surge in truth dissemination which will greatly limit the diffusion of rumor. Furthermore, in the case of unknown λ, increasing β can effectively reduce the maximum proportion of agents who do not know the truth, but cannot narrow the rumor diffusion range in a certain interval of β. [ABSTRACT FROM AUTHOR]

Online social network has become an indispensable communication tool in the information age. The development of microblog also provides us a great opportunity to study human dynamics that play a crucial role in the design of efficient communication systems. In this paper we study the characteristics of the tweeting behavior based on the data collected from Sina Microblog. The user activity level is measured to characterize how often a user posts a tweet. We find that the user activity level follows a bimodal distribution. That is, the microblog users tend to be either active or inactive. The inter-tweeting time distribution is then measured at both the aggregate and individual levels. We find that the inter-tweeting time follows a piecewise power law distribution of two tails. Furthermore, the exponents of the two tails have different correlations with the user activity level. These findings demonstrate that the dynamics of the tweeting behavior are heterogeneous in different time scales. We then develop a dynamic model co-driven by the memory and the interest mechanism to characterize the heterogeneity. The numerical simulations validate the model and verify that the short time interval tweeting behavior is driven by the memory mechanism while the long time interval behavior by the interest mechanism. [ABSTRACT FROM AUTHOR]

Routing in mobile social networks is a challenging task due to the characteristic of intermittent connectivity, especially when the nodes behave selfishly in real world. Selfish behaviors of node always influence its altruism to provide forwarding service for others and degrade network performance strongly. In this paper, to address the selfishness problem in MSNs, we propose a service-based selfish routing protocol, SSR. When making forwarding decision, SSR employ user altruism and the amount of service that the relay nodes provide. User altruism is determined by the social selfishness and the individual selfishness. The services include pairwise services and social services, which is also considered as the incentives to stimulate node to be more cooperative. The more services the node provides, the more chance the node has to be served. The node with higher altruism and fewer services is the preferred relay node. Simulation results show SSR achieves better performance when the user altruism is low and demonstrate the effectiveness of the service-based scheme. [ABSTRACT FROM AUTHOR]

A proper understanding of how complex networks grow is important to get insights into the network structure, make predictions of future growth, and enable simulation of large networks. In this paper, we focus on social networks and try to understand, capture and predict dynamic behaviors on social networks. How social networks evolve, i.e. how individuals create and deactivate social ties? It is interesting for several areas such as marketing, web search and recommendation. We propose an agent-based model in which agents represent individuals and social networks evolve driven by events and interests. The model, which we call EIM (Event-Interest Model), incorporates an intuitive idea: individuals begin to interact when participating in the same event, and social ties are formed or reinforced between two individuals if they have similar interests and would like to link to each other. Our model matches better to realistic network structure in terms of a number of statistical properties and critical patterns compared with some recent models for social networks, which suggests that both events and interests may play an important role in shaping the evolution of social networks. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper explores effective seeding strategies in prisoner's dilemma game (PDG) on online social networks, i.e. the optimal strategy to obtain global cooperation with minimum cost. Three distinct seeding strategies are compared by performing computer simulations on real online social network datasets. Our finding suggests that degree centrality seeding outperforms other strategies regardless of the initial payoff setting or network size. Celebrities of online social networks play key roles in preserving cooperation. [ABSTRACT FROM AUTHOR]

Social networking service (SNS) is one of the major technological applications based on Web 2.0, which can help users to express their views and share information with others. How to describe the information diffusion process in online SNS accurately has attracted considerable interest recently. However, almost all existing models focus on a single online SNS tool and face many challenges with multiple online SNS tools. In this paper, we turn to users' privacy setting policies and propose a general stochastic model with multiple diffusion mechanisms in online SNS, called DMPS. Specifically, we first define a privacy protection mechanism based on information sharing in online SNS and classify nodes according to different privacy setting policies; then, we define the states of the nodes and information dissemination rules with dynamics of infectious disease; finally, we describe the evolution process of different nodes by dynamic evolution equations. Detailed simulations and analysis show that the DMPS can precisely describe the diffusion process with multiple diffusion mechanisms and have the same characteristics as a diffusion process in real online SNS. As a result, DMPS can be used to identify the underlying diffusion mechanism of information and forecast its trend in online SNS. [ABSTRACT FROM AUTHOR]

Social network has extended its popularity from the Internet to mobile domain. Personal mobile devices can be self-organized and communicate with each other for instant social activities at any time and in any places to achieve pervasive social networking (PSN). In such a network, various content information flows. To which extent should mobile users trust it, whilst user privacy can also be preserved? Existing work has not yet seriously considered trust and reputation management, although trust plays an important role in PSN. In this paper, we propose PerContRep, a practical reputation system for pervasive content services that can assist trustworthy content selection and consumption in a pervasive manner. We develop a hybrid trust and reputation management model to evaluate node recommendation trust and content reputation in the context of frequent change of node pseudonyms. Simulations show the advantages of PerContRep in assisting user decisions and its effectiveness with regard to unfair rating attack, collaborative unfair rating attack, on-off attack and conflict behavior attack. A prototype system achieves positive user feedback on its usability and social acceptance. [ABSTRACT FROM AUTHOR]

Highlights: [•] We established the index system of the influence for complaint texts and themes. [•] The paper proposed a model to measure complaint texts and themes’ influence. [•] The model can be used to identify influential complaint themes and complainers. [•] It provided theory basis and effective way to manage online groups complaints. [ABSTRACT FROM AUTHOR]

SUMMARY Research on social networks has received remarkable attention, because an increasing number of people use social networks to broadcast information and stay connected with their friends. However, because of 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 proposes models to capture the characters such as the network, the user behaviors, and the process of information diffusion under information overload. The term type influence is introduced to characterize the information diffusion efficiency for users of a given type, which can be analyzed theoretically on the basis of the proposed models. Having noticed the inaccuracy of using type influence to estimate the information diffusion efficiency for a given user, we further introduce the term individual influence and propose a scalable approach to estimate it. We verify the accuracy of this approach by simulations and show that considering more nearby users leads to more computational costs, but more accurate results. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: The consensus formation process in a social network is affected by a number of factors. This paper studies how the degree mixing pattern of a social network affects the consensus formation process. A social network of more than 50,000 nodes was sampled from the online social services website Twitter. Nodes in the Twitter user network are grouped by their in-degrees and out-degrees. A degree mixing correlation is proposed to measure the randomness of the mixing pattern for each degree group. The DeGroot model is used to simulate the consensus formation processes in the network. Simulation suggests that the non-random degree mixing pattern of social networks can slow down the rate of consensus. [Copyright &y& Elsevier]

The rapid development of smartphone technology and the Internet services in mobile devices facilitates easy access to online social networking (OSN) sites anytime, anywhere. At the same time, this allures the adversaries to exploit the OSNs as a soft target for easy execution of various attacks that can quickly spread to a large number of users. In distributed denial-of-service (DDoS) attacks, an adversary aims to overwhelm the normal traffic of a targeted server with a flood of fake login messages so that the associated Internet service or website turns inoperable. In this paper, we propose a secure and lightweight authentication scheme (P R D o S) that resists DDoS and other security attacks in mobile OSN environments. We provide a multi-faceted solution towards the remedy of DDoS attacks in the OSN environment. After a certain threshold, the scheme discards further user login attempts and blocks an adversary who intends to overload the network server. We use the pre-loaded shadow identity and emergency key pairs, and a key-refilling strategy that rebuilds the essential synchronization between a blocked naive user and the OSN server. This technique restores the intended un-linkability property of the protocol. Using NS3 simulation, we study the impact of DDoS attackers on network throughput and network delay. Moreover, we validate and compare the proposed scheme against state-of-the-art solutions using the real attacks and benign datasets. We use the Canadian Institute for Cybersecurity (CIC) DoS dataset 2017, which is generated by capturing the normal and DoS attack packets separately with subsequent pre-processed for testing. We also use the machine learning (ML) algorithms, such as K-Nearest Neighbor (KNN), Gaussian Naive Bayes, and Multilayer Perceptron (MLP) to demonstrate the performance of the proposed solution in a practical attack detection scenario. We observe that these algorithms provide 97.05%, 95.48%, and 96.6% DDoS attack detection accuracy, respectively. [ABSTRACT FROM AUTHOR]

Inferring the pair-wise trust relationship is a core building block for many real applications. State-of-the-art approaches for such trust inference mainly employ the transitivity property of trust by propagating trust along connected users, but largely ignore other important properties such as trust bias, multi-aspect, etc. In this paper, we propose a new trust inference model to integrate all these important properties. To apply the model to both binary and continuous inference scenarios, we further propose a family of effective and efficient algorithms. Extensive experimental evaluations on real data sets show that our method achieves significant improvement over several existing benchmark approaches, for both quantifying numerical trustworthiness scores and predicting binary trust/distrust signs. In addition, it enjoys linear scalability in both time and space. [ABSTRACT FROM PUBLISHER]