Information Diffusion In Social Networks Research Articles

Lie algebraic discussion for affinity based information diffusion in social networks

Abstract In this paper we develop a dynamical information diffusion model which features the affinity of people with information disseminated in social networks. Four types of agents, i.e., susceptible, informed, known, and refractory ones, are involved in the system, and the affinity mechanism composing of an affinity threshold which represents the fitness of information to be propagated is incorporated. The model can be generally described by a time-inhomogeneous Markov chain, which is governed by its master (Kolmogorov) equation. Based on the Wei-Norman method, we derive analytical solutions of the model by constructing a low-dimensional Lie algebra. Numerical examples are provided to illustrate the obtained theoretical results. This study provides useful insights into the closed-form solutions of complex social dynamics models through the Lie algebra method.

Activity Maximization by Effective Information Diffusion in Social Networks

In a social network, even about the same information the excitements between different pairs of users are different. If you want to spread a piece of new information and maximize the expected total amount of excitements, which seed users should you choose? This problem indeed is substantially different from the renowned influence maximization problem and cannot be tackled using the existing approaches. In this paper, motivated by the demand in a few interesting applications, we model the novel problem of activity maximization. We tackle the problem systematically. We first analyze the complexity and the approximability of the problem. We develop an upper bound and a lower bound that are submodular so that the Sandwich framework can be applied. We then devise a polling-based randomized algorithm that guarantees a data dependent approximation factor. Our experiments on three real data sets clearly verify the effectiveness and scalability of our method, as well as the advantage of our method against the other heuristic methods.

Modeling the reemergence of information diffusion in social network

Information diffusion in networks is an important research topic in various fields. Existing studies either focus on modeling the process of information diffusion, e.g., independent cascade model and linear threshold model, or investigate information diffusion in networks with certain structural characteristics such as scale-free networks and small world networks. However, there are still several phenomena that have not been captured by existing information diffusion models. One of the prominent phenomena is the reemergence of information diffusion, i.e., a piece of information reemerges after the completion of its initial diffusion process. In this paper, we propose an optimized information diffusion model by introducing a new informed state into traditional susceptible–infected–removed model. We verify the proposed model via simulations in real-world social networks, and the results indicate that the model can reproduce the reemergence of information during the diffusion process.

Threat From Being Social: Vulnerability Analysis of Social Network Coupled Smart Grid

Social Networks (SNs) have been gradually applied by utility companies as an addition to smart grid and are proved to be helpful in smoothing load curves and reducing energy usage. However, SNs also bring in new threats to smart grid: misinformation in SNs may cause smart grid users to alter their demand, resulting in transmission line overloading and in turn leading to catastrophic impact to the grid. In this paper, we discuss the interdependency in the social network coupled smart grid and focus on its vulnerability. That is, how much can the smart grid be damaged when misinformation related to it diffuses in SNs? To analytically study the problem, we propose the Misinformation Attack Problem in Social-Smart Grid (MAPSS) that identifies the top critical nodes in the SN, such that the smart grid can be greatly damaged when misinformation propagates from those nodes. This problem is challenging as we have to incorporate the complexity of the two networks concurrently. Nevertheless, we propose a technique that can explicitly take into account information diffusion in SN, power flow balance and cascading failure in smart grid integratedly when evaluating node criticality, based on which we propose various strategies in selecting the most critical nodes. Also, we introduce controlled load shedding as a protection strategy to reduce the impact of cascading failure. The effectiveness of our algorithms are demonstrated by experiments on IEEE bus test cases as well as the Pegase data set.

Efficient Online Summarization of Large-Scale Dynamic Networks

Information diffusion in social networks is often characterized by huge participating communities and viral cascades of high dynamicity. To observe, summarize, and understand the evolution of dynamic diffusion processes in an informative and insightful way is a challenge of high practical value. However, few existing studies aim to summarize networks for interesting dynamic patterns. Dynamic networks raise new challenges not found in static settings, including time sensitivity, online interestingness evaluation, and summary traceability, which render existing techniques inadequate. We propose dynamic network summarization to summarize dynamic networks with millions of nodes by only capturing the few most interesting nodes or edges over time. Based on the concepts of diffusion radius and scope, we define interestingness measures for dynamic networks, and we propose $\sf {OSNet}$ , an online summarization framework for dynamic networks. Efficient algorithms are included in $\sf {OSNet}$ . We report on extensive experiments with both synthetic and real-life data. The study offers insight into the effectiveness, efficiency, and design properties of $\sf {OSNet}$ .

Recommendation information diffusion in social networks considering user influence and semantics

One of the major problems in the domain of social networks is the handling and diffusion of the vast, dynamic and disparate information created by its users. In this context, the information contributed by users can be exploited to generate recommendations for other users. Relevant recommender systems take into account static data from users’ profiles, such as location, age or gender, complemented with dynamic aspects stemming from the user behavior and/or social network state such as user preferences, items’ general acceptance and influence from social friends. In this paper, we enhance recommendation algorithms used in social networks by taking into account qualitative aspects of the recommended items, such as price and reliability, the influencing factors between social network users, the social network user behavior regarding their purchases in different item categories and the semantic categorization of the products to be recommended. The inclusion of these aspects leads to more accurate recommendations and diffusion of better user-targeted information. This allows for better exploitation of the limited recommendation space, and therefore, online advertisement efficiency is raised.

Optimal Resource Allocation Over Time and Degree Classes for Maximizing Information Dissemination in Social Networks

We study the optimal control problem of allocating campaigning resources over the campaign duration and degree classes in a social network. Information diffusion is modeled as a Susceptible-Infected epidemic and direct recruitment of susceptible nodes to the infected (informed) class is used as a strategy to accelerate the spread of information. We formulate an optimal control problem for optimizing a net reward function, a linear combination of the reward due to information spread and cost due to application of controls. The time varying resource allocation and seeds for the epidemic are jointly optimized. A problem variation includes a fixed budget constraint. We prove the existence of a solution for the optimal control problem, provide conditions for uniqueness of the solution, and prove some structural results for the controls (e.g. controls are non-increasing functions of time). The solution technique uses Pontryagin's Maximum Principle and the forward-backward sweep algorithm (and its modifications) for numerical computations. Our formulations lead to large optimality systems with up to about 200 differential equations and allow us to study the effect of network topology (Erdos-Renyi/scale-free) on the controls. Results reveal that the allocation of campaigning resources to various degree classes depends not only on the network topology but also on system parameters such as cost/abundance of resources. The optimal strategies lead to significant gains over heuristic strategies for various model parameters. Our modeling approach assumes uncorrelated network, however, we find the approach useful for real networks as well. This work is useful in product advertising, political and crowdfunding campaigns in social networks.

A novel game theoretic approach for modeling competitive information diffusion in social networks with heterogeneous nodes

Abstract Influence maximization deals with identification of the most influential nodes in a social network given an influence model. In this paper, a game theoretic framework is developed that models a competitive influence maximization problem. A novel competitive influence model is additionally proposed that incorporates user heterogeneity, message content, and network structure. The proposed game-theoretic model is solved using Nash Equilibrium in a real-world dataset. It is shown that none of the well-known strategies are stable and at least one player has the incentive to deviate from the proposed strategy. Moreover, violation of Nash equilibrium strategy by each player leads to their reduced payoff. Contrary to previous works, our results demonstrate that graph topology, as well as the nodes’ sociability and initial tendency measures have an effect on the determination of the influential node in the network.

Information diffusion in social sensing

Statistical inference using social sensors is an area that has witnessed remarkable progress in the last decade. It is relevant in a variety of applications including localizing events for targeted advertising, marketing, localization of natural disasters and predicting sentiment of investors in financial markets. This paper presents a tutorial description of three important aspects of sensing-based information diffusion in social networks from a communications/signal processing perspective. First, diffusion models for information exchange in large scale social networks together with social sensing via social media networks such as Twitter is considered. Second, Bayesian social learning models in online reputation systems are presented. Finally, the principle of revealed preferences arising in micro-economics theory is used to parse datasets to determine if social sensors are utility maximizers and then determine their utility functions. All three topics are explained in the context of actual experimental datasets from health networks, social media and psychological experiments. Also, algorithms are given that exploit the above models to infer underlying events based on social sensing. The overview, insights, models and algorithms presented in this paper stem from recent developments in computer-science, economics, psychology and electrical engineering.

Mining communities in social network based on information diffusion

Based on the locality of information diffusion in social network, a new community detection algorithm DC_ID is proposed in this paper. Different from that of the traditional community discovery algorithms, the goal of the DC_ID algorithm is that the difference between the node's influence degree in its community and its influence degree in the whole network is small, which paves the way for further research on the influence maximization problem based on the detected community structure. Moreover, the algorithm can tackle the community detecting program in directed and weighted networks, which makes it different from other existing algorithms. The algorithm consists of two stages: partition and combination. During the first stage, the algorithm first estimates all nodes' influence in the network, then chooses the most influential non‐community node as the seed, and expands its influence along diffusion paths layer by layer. Finally, the local community with the node as its core is found. During the second stage, the closeness of two local communities will be evaluated by their ‘combination entropy’. When the combination entropy of any two communities is higher than some predefined threshold, they will be combined into one. To evaluate the quality of the detected community structure, two new measures, LEW and CRC, are introduced. Empirical studies on three real‐world social networks show that the algorithm outperforms the benchmark algorithm in runtime, LEW, and CRC. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

A framework for information dissemination in social networks using Hawkes processes

We define in this paper a general Hawkes-based framework to model information diffusion in social networks. The proposed framework takes into consideration the hidden interactions between users as well as the interactions between contents and social networks, and can also accommodate dynamic social networks and various temporal effects of the diffusion, which provides a complete analysis of the hidden influences in social networks. This framework can be combined with topic modeling, for which modified collapsed Gibbs sampling and variational Bayes techniques are derived. We provide an estimation algorithm based on nonnegative tensor factorization techniques, which together with a dimensionality reduction argument are able to discover, in addition, the latent community structure of the social network. At last, we provide numerical examples from real-life networks: a Game of Thrones and a MemeTracker datasets.

Advancement from Topic based to Information based Model: A Survey

Social Network played a vital role in diffusing information at a very large scale, lot of work has been done in this area to explain and understand this phenomenon, classifying from predominant topic detection to information diffusion modeling, containing prominent diffuser's identification. This paper presents a survey of illustrative procedures that deal with these topics and propose a classification that reviews the state-of-art. The aim of this paper is to provide an extensive analysis of prevailing efforts around information diffusion in social networks is the aim of the paper. This survey is projected to assist scholars to understand it quickly and bring the enhancement in the present work.

Identifying Topic-sensitive Influential Spreaders in Social Networks

Identifying influential spreaders is an important issue in understanding the dynamics of information diffusion in social networks. It is to find a small subset of nodes, which can spread the information or influence to the largest number of nodes. The conventional approaches consider information diffusion through the network in a coarse-grained manner, without taking into account the topical features of information content and users. However, for messages with different topics, the target influential spreaders may vary largely. In this paper, we propose to harness historical propagation data to learn the information diffusion probabilities on topic-level, based on which we use a greedy algorithm to iteratively select a set of influential nodes for a given topic. Specially, we design a three-stage algorithm named TopicRank to mine the most influential spreaders with respect to a specific topic. Given observed propagation data, we first use Latent Dirichlet Allocation (LDA) model to learn a topic mixture for each propagation message. Then, the topic-level diffusion probability of an edge is computed by exploiting the propagation actions occurred to it and the topic distribution of these propagation messages. Last, based on the learned topic-level diffusion probabilities, we apply optimized greedy algorithm CLEF to identify influential nodes with respect to a specific topic. Experimental results show that our method significantly outperforms state-of-the-art methods when used for topic-sensitive information spread maximization.

An enriched social behavioural information diffusion model in social networks

Online social networks have recently become an innovative and effective method for spreading information among people around the world. Information diffusion, rumour spreading and diseases infection are all instances of stochastic processes that occur over the edges of social networks. Many prior works have carried out empirical studies and diffusion models to understand how information propagates in online social networks; however they suffer from problems. In this paper, we propose an information diffusion model inspired by information propagation among people. Our proposed Social Behavioural Information Diffusion Model, abbreviated as SBIDM, considers the effect of mainstream media like TV and radio, as well as interaction with the neighbours. The advantages of our approach are four-fold. First, it models information diffusion in social networks inspired by social life, which considers the effect of aggregate social behaviour to diffuse information; second, it allows partial knowledge to be held in each individual; third, it considers the effects of social media in propagating information as well as the effects of interacting with neighbours; and last but not least, it is applicable to different types of data including synthetic and well-known real social networks like Facebook, Amazon, Epinions and DBLP. To explore the advantages of our approach, many experiments with different settings and specifications were conducted. The obtained results are very promising.

Health behavior spreading with similar diminishing returns effect

Nowadays the propagation of human activity, especially health behavior, has become a significant issue in the study of online social network. Considering the effect of human behavior on information diffusion in social networks (Centola, 2010), we propose a simple “susceptible–adopted–susceptible” model with several interpretable parameters. This model shows a similar diminishing returns effect, in which the adoption probability of a susceptible individual grows less quickly as the number of his or her adopted neighbors increases. Surprisingly, the model’s contagion threshold is merely decided by the spreading rate and logout probability, not affected by the similar diminishing returns effect in real cases. Moreover, we find that the augmentation of the similar diminishing returns factor can increase the spreading speed and coverage. Our model captures properties of the real spreading of health behavior in real online networks and can provide instructions for fast propagation of new health behavior.

Partial differential equations with Robin boundary condition in online social networks

In recent years, online social networks such as Twitter, have become a major source of information exchange and research on information diffusion in social networks has been accelerated. Partial differential equations are proposed to characterize temporal and spatial patterns of information diffusion over online social networks. The new modeling approach presents a new analytic framework towards quantifying information diffusion through the interplay of structural and topical influences. In this paper we develop a non-autonomous diffusive logistic model with indefinite weight and the Robin boundary condition to describe information diffusion in online social networks. It is validated with a real dataset from an online social network, Digg.com. The simulation shows that the logistic model with the Robin boundary condition is able to more accurately predict the density of influenced users. We study the bifurcation, stability of the diffusive logistic model with heterogeneity in distance. The bifurcation and stability results of the model information describe either information spreading or vanishing in online social networks.

Modeling and Analysis of Information Dissemination in Online Social Networks

Information diffusion in online social networks has attracted public attention, and epidemic models have been applied to describe the diffusion process. However, online social networks exhibit a different propagation mechanism. To better characterize online information diffusion, we put forward a new model in which agents spontaneously participate in the discussions regarding a topic or may be persuaded by neighbors. Agents may ignore the information once they contact it. The attraction of the topic decays with time, and spreaders may also lose their interest in diffusing information. A real social network and virtual scale-free network are used as interaction topology. Results show unlike traditional epidemic models, the threshold of the interpersonal spreading rate with spontaneous diffusion drops to zero. The spontaneous diffusion mechanism lowers the threshold of diffusion process, but cannot cause a large extent of infection. The effect of the average network degree is quite different from that of the network diameter. The average degree of underlying network greatly improves the density of spreaders for any value of the interpersonal spreading rate, but the network diameter has a clear effect on the dynamics only when the spreading rate is large. Our work can help to understand and make effective measures of promoting or prohibiting information diffusion in social networks.

Information Dissemination of Public Health Emergency on Social Networks and Intelligent Computation.

Due to the extensive social influence, public health emergency has attracted great attention in today's society. The booming social network is becoming a main information dissemination platform of those events and caused high concerns in emergency management, among which a good prediction of information dissemination in social networks is necessary for estimating the event's social impacts and making a proper strategy. However, information dissemination is largely affected by complex interactive activities and group behaviors in social network; the existing methods and models are limited to achieve a satisfactory prediction result due to the open changeable social connections and uncertain information processing behaviors. ACP (artificial societies, computational experiments, and parallel execution) provides an effective way to simulate the real situation. In order to obtain better information dissemination prediction in social networks, this paper proposes an intelligent computation method under the framework of TDF (Theory-Data-Feedback) based on ACP simulation system which was successfully applied to the analysis of A (H1N1) Flu emergency.

Evolutionary Game Analysis of Competitive Information Dissemination on Social Networks: An Agent-Based Computational Approach

Social networks are formed by individuals, in which personalities, utility functions, and interaction rules are made as close to reality as possible. Taking the competitive product-related information as a case, we proposed a game-theoretic model for competitive information dissemination in social networks. The model is presented to explain how human factors impact competitive information dissemination which is described as the dynamic of a coordination game and players’ payoff is defined by a utility function. Then we design a computational system that integrates the agent, the evolutionary game, and the social network. The approach can help to visualize the evolution of % of competitive information adoption and diffusion, grasp the dynamic evolution features in information adoption game over time, and explore microlevel interactions among users in different network structure under various scenarios. We discuss several scenarios to analyze the influence of several factors on the dissemination of competitive information, ranging from personality of individuals to structure of networks.

Topology dependent epidemic spreading velocity in weighted networks

Many diffusive processes occur on structured networks with weighted links, such as disease spread by airplane transport or information diffusion in social networks or blogs. Understanding the impact of weight-connectivity correlations on epidemic spreading in weighted networks is crucial to support decision-making on disease control and other diffusive processes. However, a real understanding of epidemic spreading velocity in weighted networks is still lacking. Here we conduct a numerical study of the velocity of a Reed–Frost epidemic spreading process in various weighted network topologies as a function of the correlations between edge weights and node degrees. We find that a positive weight-connectivity correlation leads to a faster epidemic spreading compared to an unweighted network. In contrast, we find that both uncorrelated and negatively correlated weight distributions lead to slower spreading processes. In the case of positive weight-connectivity correlations, the acceleration of spreading velocity is weak when the heterogeneity of weight distribution increases.