Capturing Dynamics of Information Diffusion in SNS: A Survey of Methodology and Techniques

Abstract

Studying information diffusion in SNS (Social Networks Service) has remarkable significance in both academia and industry. Theoretically, it boosts the development of other subjects such as statistics, sociology, and data mining. Practically, diffusion modeling provides fundamental support for many downstream applications (\textit{e.g.}, public opinion monitoring, rumor source identification, and viral marketing.) Tremendous efforts have been devoted to this area to understand and quantify information diffusion dynamics. This survey investigates and summarizes the emerging distinguished works in diffusion modeling. We first put forward a unified information diffusion concept in terms of three components: information, user decision, and social vectors, followed by a detailed introduction of the methodologies for diffusion modeling. And then, a new taxonomy adopting hybrid philosophy (\textit{i.e.,} granularity and techniques) is proposed, and we made a series of comparative studies on elementary diffusion models under our taxonomy from the aspects of assumptions, methods, and pros and cons. We further summarized representative diffusion modeling in special scenarios and significant downstream tasks based on these elementary models. Finally, open issues in this field following the methodology of diffusion modeling are discussed.

arXiv commentAuthor version, with 50 pages, 6 figures, 16 tables, and 5 algorithms

References in corpus (38)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• The structure and function of complex networks
• Auto-Encoding Variational Bayes
• Epidemic spreading in scale-free networks
• Epidemic processes in complex networks
• Robust dynamic classes revealed by measuring the response function of a social system
• Generation of uncorrelated random scale-free networks
• Kronecker Graphs: An Approach to Modeling Networks
• Detection and Resolution of Rumours in Social Media: A Survey
• DeepInf: Social Influence Prediction with Deep Learning
• Can Cascades be Predicted?
• SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity
• Information diffusion epidemics in social networks
• Uncovering the Temporal Dynamics of Diffusion Networks
• Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign
• Epidemic spreading in complex networks with degree correlations
• Community structure and scale-free collections of Erdös-Rényi graphs
• Quantifying the Effect of Sentiment on Information Diffusion in Social Media
• A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances
• On the Convexity of Latent Social Network Inference
• Evolutionary Information Diffusion over Social Networks
• Time-Critical Influence Maximization in Social Networks with Time-Delayed Diffusion Process
• Collective phenomena emerging from the interactions between dynamical processes in multiplex networks
• A Scalable Generative Graph Model with Community Structure
• Multimodal Generative Models for Scalable Weakly-Supervised Learning
• Evolutionary Dynamics of Information Diffusion over Social Networks
• Triadic Measures on Graphs: The Power of Wedge Sampling
• Feature Driven and Point Process Approaches for Popularity Prediction
• Modeling and Predicting Popularity Dynamics via Reinforced Poisson Processes
• Expecting to be HIP: Hawkes Intensity Processes for Social Media Popularity
• TiDeH: Time-Dependent Hawkes Process for Predicting Retweet Dynamics
• Inf-VAE: A Variational Autoencoder Framework to Integrate Homophily and Influence in Diffusion Prediction
• Lurking in Social Networks: Topology-based Analysis and Ranking Methods
• Time-aware Analysis and Ranking of Lurkers in Social Networks
• Submodular Inference of Diffusion Networks from Multiple Trees
• Network Modularity Controls the Speed of Information Diffusion
• From Competition to Complementarity: Comparative Influence Diffusion and Maximization
• A Survey on Personality-Aware Recommendation Systems