Related papers: Influencing dynamics on social networks without kn…
The rapid dissemination of misinformation through online social networks poses a growing threat to public understanding and societal stability. Prebunking, a proactive strategy based on inoculation theory, has recently emerged as an…
Mastering the dynamics of social influence requires separating, in a database of information propagation traces, the genuine causal processes from temporal correlation, i.e., homophily and other spurious causes. However, most studies to…
One major feature of social networks (e.g., massive online social networks) is the dissemination of information, such as news, rumors and opinions. Information can be propagated via natural connections in written, oral or electronic forms.…
Identifying influential nodes plays a pivotal role in understanding, controlling, and optimizing the behavior of complex systems, ranging from social to biological and technological domains. Yet most centrality-based approaches rely on…
Social dynamics is concerned primarily with interactions among individuals and the resulting group behaviors, modeling the temporal evolution of social systems via the interactions of individuals within these systems. In particular, the…
This study considers a simple variation of the voter model with two competing parties. In particular, we represent the case of political elections, where people can choose to support one of the two candidates or to remain neutral. People…
Decision-making on networks can be explained by both homophily and social influence. While homophily drives the formation of communities with similar characteristics, social influence occurs both within and between communities. Social…
The Ising models have been applied for various problems on information sciences, social sciences, and so on. In many cases, solving these problems corresponds to minimizing the Bethe free energy. To minimize the Bethe free energy, a…
Most infectious diseases spread on a dynamic network of human interactions. Recent studies of social dynamics have provided evidence that spreading patterns may depend strongly on detailed micro-dynamics of the social system. We have…
This paper provides a simple theoretical framework to evaluate the effect of key parameters of ranking algorithms, namely popularity and personalization parameters, on measures of platform engagement, misinformation and polarization. The…
In recent years, recommendation systems have been widely applied in many domains. These systems are impotent in affecting users to choose the behavior that the system expects. Meanwhile, providing incentives has been proven to be a more…
Coupled Ising models are studied in a discrete choice theory framework, where they can be understood to represent interdependent choice making processes for homogeneous populations under social influence. Two different coupling schemes are…
Understanding the dynamics of spatially extended systems represents a challenge in diverse scientific disciplines, ranging from physics and mathematics to the earth and climate sciences or the neurosciences. This challenge has stimulated…
The diffusion of information and behaviors over social networks is of considerable interest in research fields ranging from sociology to computer science and application domains such as marketing, finance, human health, and national…
The focus of this paper is modeling what we call a Social Radar, i.e. a method to estimate the relative influence between social agents, by sampling their opinions and as they evolve, after injecting in the network stubborn agents. The…
It is commonly believed that patterns of social ties affect individuals' economic status. Here, we translate this concept into an operational definition at the network level, which allows us to infer the economic wellbeing of individuals…
In today's world, individuals interact with each other in more complicated patterns than ever. Some individuals engage through online social networks (e.g., Facebook, Twitter), while some communicate only through conventional ways (e.g.,…
An ability to infer the political leaning of social media users can help in gathering opinion polls thereby leading to a better understanding of public opinion. While there has been a body of research attempting to infer the political…
Influence systems form a large class of multiagent systems designed to model how influence, broadly defined, spreads across a dynamic network. We build a general analytical framework which we then use to prove that, while sometimes chaotic,…
Causal effect estimation in networked systems is central to data-driven decision making. In such settings, interventions on one unit can spill over to others, and in complex physical or social systems, the interaction pathways driving these…