Measuring Social Influence with Networked Synthetic Control
Measuring social influence is difficult due to the lack of counter-factuals and comparisons. By combining machine learning-based modeling and network science, we present general properties of social value, a recent measure for social influence using synthetic control applicable to political behavior. Social value diverges from centrality measures on in that it relies on an external regressor to predict an output variable of interest, generates a synthetic measure of influence, then distributes individual contribution based on a social network. Through theoretical derivations, we show the properties of SV under linear regression with and without interaction, across lattice networks, power-law networks, and random graphs. A reduction in computation can be achieved for any ensemble model. Through simulation, we find that the generalized friendship paradox holds -- that in certain situations, your friends have on average more influence than you do.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Does Socialization Emerge in AI Agent Society? A Case Study of Moltbook
As large language model agents increasingly populate networked environments, a fundamental question arises: do artificial intelligence (AI) agent societies undergo convergence dynamics similar to human social systems? La…
Treatment Effects Estimation on Networked Observational Data using Disentangled Variational Graph Autoencoder
Estimating individual treatment effect (ITE) from observational data has gained increasing attention across various domains, with a key challenge being the identification of latent confounders affecting both treatment an…
Representation LearningDigital Twin-Oriented Complex Networked Systems based on Heterogeneous Node Features and Interaction Rules
This study proposes an extendable modelling framework for Digital Twin-Oriented Complex Networked Systems (DT-CNSs) with a goal of generating networks that faithfully represent real systems. Modelling process focuses on …
DiversityGraph-Based Methods for Discrete Choice
Choices made by individuals have widespread impacts--for instance, people choose between political candidates to vote for, between social media posts to share, and between brands to purchase--moreover, data on these choi…
Discrete Choice ModelsGraph LearningTag Relevance Fusion for Social Image Retrieval
Due to the subjective nature of social tagging, measuring the relevance of social tags with respect to the visual content is crucial for retrieving the increasing amounts of social-networked images. Witnessing the limit …
Image RetrievalRetrievalTAG