Causal Influences over Social Learning Networks
This work addresses the problem of understanding and quantifying influence in social networks for researchers and practitioners in distributed systems and social network analysis, though it appears incremental as it builds on existing social learning models.
The paper investigates causal influences between agents in social learning networks, deriving expressions that reveal causal relations and influence flow dependent on graph topology and agent information levels, and proposes an algorithm to rank agent influence and learn model parameters from observational data, illustrated with synthetic and real Twitter data.
This paper investigates causal influences between agents linked by a social graph and interacting over time. In particular, the work examines the dynamics of social learning models and distributed decision-making protocols, and derives expressions that reveal the causal relations between pairs of agents and explain the flow of influence over the network. The results turn out to be dependent on the graph topology and the level of information that each agent has about the inference problem they are trying to solve. Using these conclusions, the paper proposes an algorithm to rank the overall influence between agents to discover highly influential agents. It also provides a method to learn the necessary model parameters from raw observational data. The results and the proposed algorithm are illustrated by considering both synthetic data and real Twitter data.