Information diffusion model with homogeneous continuous time Markov chain on Indonesian Twitter users

In this paper, a homogeneous continuous time Markov chain (CTMC) is used to model information diffusion or dissemination, also to determine influencers on Twitter dynamically. The tweeting process can be modeled with a homogeneous CTMC since the properties of Markov chains are fulfilled. In this case, the tweets that are received by followers only depend on the tweets from the previous followers. Knowledge Discovery in Database (KDD) in Data Mining is used to be research methodology including pre-processing, data mining process using homogeneous CTMC, and post-processing to get the influencers using visualization that predicts the number of affected users. We assume the number of affected users follows a logarithmic function. Our study examines the Indonesian Twitter data users with tweets about covid19 vaccination resulted in dynamic influencer rankings over time. From these results, it can also be seen that the users with the highest number of followers are not necessarily the top influencer.publishedVersio

Infection Spreading and Source Identification: A Hide and Seek Game

The goal of an infection source node (e.g., a rumor or computer virus source) in a network is to spread its infection to as many nodes as possible, while remaining hidden from the network administrator. On the other hand, the network administrator aims to identify the source node based on knowledge of which nodes have been infected. We model the infection spreading and source identification problem as a strategic game, where the infection source and the network administrator are the two players. As the Jordan center estimator is a minimax source estimator that has been shown to be robust in recent works, we assume that the network administrator utilizes a source estimation strategy that can probe any nodes within a given radius of the Jordan center. Given any estimation strategy, we design a best-response infection strategy for the source. Given any infection strategy, we design a best-response estimation strategy for the network administrator. We derive conditions under which a Nash equilibrium of the strategic game exists. Simulations in both synthetic and real-world networks demonstrate that our proposed infection strategy infects more nodes while maintaining the same safety margin between the true source node and the Jordan center source estimator

On the coexistence of competing memes in the same social network

We present a model of double viral spreading on a multi-network suitable for modeling the diffusion of competitive memes. The main characteristics of the model is that it allows the possibility of changing from one opinion to another without having to go through an agnostic (susceptible) phase. We analyze the survival of two conflicting memes in the limit of t→∞ as a function of the viralities of the two memes as well as the rate at which an agent can change from believing one meme to believing the other. The parameter space is divided into four survival regions, depending on which meme survives (none, one of the two, both). We derive exact equations for the boundaries between the regions in the case of regular graphs, and, in the general case, we characterize the behavior for weak and strong memes. One relevant finding is that, contrary to the viral spread model in Sahneh and Scoglio (2014), the crossover allows the co-existence of two memes in the same network. We present simulations to validate the theoretical resultsPID2019-108965GB-I0

A review on graphical evolutionary game for information diffusion on social networks

Purpose - With the recent development of science and technology, research on information diffusion has become increasingly important. Design/methodology/approach - To analyze the process of information diffusion, researchers have proposed a framework with graphical evolutionary game theory (EGT) according to the theory of biological evolution. Findings - Through this method, one can study and even predict information diffusion. Originality/value - This paper summarizes three existing works using graphical EGT to discuss how to obtain the static state and the dynamics of information diffusion in social network