Modeling Social Influence and Network Interference in Psychological Research
Social network analysis can be used to model how individuals' beliefs, attitudes, and behaviors are influenced by their social connections, and how this social influence can interfere with experimental interventions.
Adaptive Social Learning Enables Discovery of Personalized Truths in Community-Structured Networks
The adaptive social learning strategy allows each community in a network to discover its own underlying truth, in contrast to traditional social learning which forces the entire network to converge to a common solution.
Emergent Group Dynamics in Wikipedia Editing: Insights from Analyzing Causal Patterns in User Interactions
The study reveals an emergent coherence in the sizes of transient groups that form to edit Wikipedia page content, with two key group sizes - around N=8 for maximum contention, and around N=4 as a regular team size. These findings align with previous predictions about social group sizes and have implications for understanding the self-organizing properties of online communities.
Open-source Software for Estimating Autologistic Actor Attribute Models (ALAAMs) for Social Influence
The autologistic actor attribute model (ALAAM) is a model for estimating parameters related to social influence or contagion processes in social networks. This work introduces ALAAMEE, open-source Python software for estimating, simulating, and evaluating ALAAM models.
Maximizing the Market Share of a New Product in a Competitive Environment through Targeted Community Formation
By strategically forming a community of targeted users and leveraging their influence, a new product can establish a positive market share even in a market dominated by an existing product.
Simulating Relational Event Histories to Assess Goodness-of-Fit and Develop Theories
Relational event simulations can be used to assess the goodness-of-fit of relational event models and to develop and test social theories about interaction dynamics in social networks.
Optimizing Multiplex Influence Maximization in Social Networks through Graph Bayesian Optimization
The core message of this paper is to introduce the Multiplex Influence Maximization (Multi-IM) problem, which aims to maximize the influence spread of multiple interconnected information items in a social network, and to propose a Graph Bayesian Optimization framework (GBIM) to effectively solve this problem.
RA-ICM: A Novel Independent Cascade Model Incorporating User Relationships and Attitudes
User relationships and attitudes significantly impact information propagation in social networks, necessitating a new model for accurate predictions.
Analyzing Influencer Archetypes in Social Networks
Automated agents in social networks can influence opinions through various archetypal styles, raising ethical concerns.
X (Twitter) Signed Ego Networks: Culture, Topics, and Sentiments
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