Trends in Social Network Analysis - Information Propagation, User Behavior Modeling, Forecasting, and Vulnerability Assessment

Rokia Missaoui (1, 2) , Talel Abdessalem (1, 2) , Mathieu Latapy (3)

1 DIG - Data, Intelligence and Graphs
2 INFRES - Département Informatique et Réseaux
3 UPMC - Université Pierre et Marie Curie - Paris 6

The book collects contributions from experts worldwide addressing recent scholarship in social network analysis such as influence spread, link prediction, dynamic network biclustering, and delurking. It covers both new topics and new solutions to known problems. The contributions rely on established methods and techniques in graph theory, machine learning, stochastic modelling, user behavior analysis and natural language processing, just to name a few. This text provides an understanding of using such methods and techniques in order to manage practical problems and situations. Trends in Social Network Analysis: Information Propagation, User Behavior Modelling, Forecasting, and Vulnerability Assessment appeals to students, researchers, and professionals working in the field.

Keywords: Social Networks Analysis; Information Propagation; User Behavior Modelling; Forecasting; Vulnerability Assessment

Domains: Computer Science [cs]; Social and Information Networks [cs.SI]; Computer Science [cs]; Machine Learning [cs.LG]; Computer Science [cs]; Information Retrieval [cs.IR]; Mathematics [math]

Rokia Missaoui, Talel Abdessalem, Mathieu Latapy. Trends in Social Network Analysis - Information Propagation, User Behavior Modeling, Forecasting, and Vulnerability Assessment. Springer International Publishing, pp.255, 2017, ⟨10.1007/978-3-319-53420-6⟩. ⟨hal-01700169⟩