Information Diffusion of Topic Propagation in Social Media
Shahin Mahdizadehaghdam, Han Wang, Hamid Krim, Liyi Dai

TL;DR
This paper models and predicts how information spreads across interconnected social media networks using layered graph structures, Kalman filtering, and real-world data, improving prediction accuracy over traditional single-layer models.
Contribution
Introduces a layered network diffusion model using supra-Laplacian matrices and Kalman filtering to enhance prediction of agent states in social media networks.
Findings
Layered network model outperforms single-layer models in prediction accuracy.
Kalman filter with partial observations further reduces prediction error.
Validation on three real-world datasets confirms effectiveness of the approach.
Abstract
Real-world social and/or operational networks consist of agents with associated states, whose connectivity forms complex topologies. This complexity is further compounded by interconnected information layers, consisting, for instance, documents/resources of the agents which mutually share topical similarities. Our goal in this work is to predict the specific states of the agents, as their observed resources evolve in time and get updated. The information diffusion among the agents and the publications themselves effectively result in a dynamic process which we capture by an interconnected system of networks (i.e. layered). More specifically, we use a notion of a supra-Laplacian matrix to address such a generalized diffusion of an interconnected network starting with the classical "graph Laplacian". The auxiliary and external input update is modeled by a multidimensional Brownian…
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Taxonomy
TopicsComplex Network Analysis Techniques · Opinion Dynamics and Social Influence · Complex Systems and Time Series Analysis
