Information Diffusion Model in Twitter: A Systematic Literature Review

Information diffusion, information spread, and influencers are important concepts in many studies on social media, especially Twitter analytics. However, literature overviews on the information diffusion of Twitter analytics are sparse, especially on the use of continuous time Markov chain (CTMC). This paper examines the following topics: (1) the purposes of studies about information diffusion on Twitter, (2) the methods adopted to model information diffusion on Twitter, (3) the metrics applied, and (4) measures used to determine influencer rankings. We employed a systematic literature review (SLR) to explore the studies related to information diffusion on Twitter extracted from four digital libraries. In this paper, a two-stage analysis was conducted. First, we implemented a bibliometric analysis using VOSviewer and R-bibliometrix software. This approach was applied to select 204 papers after conducting a duplication check and assessing the inclusion–exclusion criteria. At this stage, we mapped the authors’ collaborative networks/collaborators and the evolution of research themes. Second, we analyzed the gap in research themes on the application of CTMC information diffusion on Twitter. Further filtering criteria were applied, and 34 papers were analyzed to identify the research objectives, methods, metrics, and measures used by each researcher. Nonhomogeneous CTMC has never been used in Twitter information diffusion modeling. This finding motivates us to further study nonhomogeneous CTMC as a modeling approach for Twitter information diffusion.

Improving mariculture insurance premium rate calculation using an information diffusion model

Mariculture is a well-known high-risk industry. However, mariculture insurance, which is an important risk management tool, is facing serious market failure. An important reason for this market failure lies in the unsound premium rate and pricing method. Due to a lack of long-term yield data, empirical rates are often adopted, but this adoption can lead to a high loss ratio. This paper provides an improved method for premium computation of mariculture insurance using an information diffusion model (IDM). An example of oyster insurance in China shows that, compared with the traditional pricing approach, the IDM can greatly improve the accuracy and stability of premium rate calculations, especially in cases of small samples.

Diffusion-Based Influence Maximization in GOLAP

Influence analysis is one of the most important research in social network. Specifically, more and more researchers and advertisers are interested in the area of influence maximization (IM). The concept of influence among people or organizations has been the core basis for making business decisions as well as performing everyday social activities. In this research, we begin by extending a new influence diffusion model information diffusion model (IDM) using various constraints. We incorporate colors and additional nodes constraints. By adding colors and constraints for different types of nodes in a graph, we would be able to answer complex queries on multi-dimensional graphs such as ‘find at most two most important genes that are related to lung disease and heart disease’. More specifically, we discuss the following variations of IM-IDM; Colorblind IM-IDM, Colored IM-IDM and Colored IM-IDM with constraints. We also present our experiment results to prove the effectiveness of our model and algorithms.

Maximizing the Influence of the Innovative Products Diffusion considering Advertisement and Promotion Strategies

With the acceleration of product updates and the intensification of product competition, product market strategies become the primary consideration for the enterprises, and the advertisement and promotion strategies are considered the two important strategies implemented by enterprises. This paper considers the enterprises of similar products and substitute, their formation of a competition between traditional products and innovative products, and establishes a mixed node-level information diffusion model to describe the dynamic product diffusion process with complex network theories. We implement advertising strategies for potential buyers who have not obtained product information and implement promotional strategies for those who have obtained product information. In accordance with Pontryagin maximization principle, we seek the best strategy to maximize the impact of innovative products and use numerical calculations to simulate the diffusion state of products. We found that the advertisement strategies play a decisive role in the marketing of innovative products. If product promotion strategies are added, the spread of innovative products will be more effective and more influential.

Cyber Physical Systems Dependability Using CPS-IOT Monitoring

Recently, vast investments have been made worldwide in developing Cyber-Physical Systems (CPS) as solutions to key socio-economic challenges. The Internet-of-Things (IoT) has also enjoyed widespread adoption, mostly for its ability to add “sensing” and “actuation” capabilities to existing CPS infrastructures. However, attention must be paid to the impact of IoT protocols on the dependability of CPS infrastructures. We address the issues of CPS dependability by using an epidemic model of the underlying dynamics within the CPS’ IoT subsystem (CPS-IoT) and an interference-aware routing reconfiguration. These help to efficiently monitor CPS infrastructure—avoiding routing oscillation, while improving its safety. The contributions of this paper are threefold. Firstly, a CPS orchestration model is proposed that relies upon: (i) Inbound surveillance and outbound actuation to improve dependability and (ii) a novel information diffusion model that uses epidemic states and diffusion sets to produce diffusion patterns across the CPS-IoT. Secondly, the proposed CPS orchestration model is numerically analysed to show its dependability for both sensitive and non-sensitive applications. Finally, a novel interference-aware clustering protocol called “INMP”, which enables network reconfiguration through migration of nodes across clusters, is proposed. It is then bench-marked against prominent IoT protocols to assess its impact on the dependability of the CPS.

RLIM: Representation Learning Method for Influence Maximization in social networks

A core issue in influence propagation is influence maximization, which aims to find a group of nodes under a specific information diffusion model and maximize the final influence of this group of nodes. The limitation of the existing researches is that they excessively depend on the information diffusion model and randomly set the propagation ability (probability). Therefore, most of the algorithms for solving the influence maximization problem are basically difficult to expand in large social networks. Another challenge is that fewer researchers have paid attention to the problem of the large difference between the estimated influence spread and the actual influence spread. A measure to solve the influence maximization problem is applying advanced neural network architecture also represents learning method. Based on this idea, the paper proposes Representation Learning for Influence Maximization (RLIM) algorithm. The premise of this algorithm is to construct the influence cascade of each source node. The key is to adopt neural network architecture to realize the prediction of propagation ability. The purpose is to apply the propagation ability to the influence maximization problem by representation learning. Furthermore, the results of the experiments show that RLIM algorithm has greater diffusion ability than the state-of-the-art algorithms on different online social network data sets, and the diffusion of information is more accurate.