Trends in Social Network Analysis

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.

Presents overviews of problems and recommended solutions in social network analysis such as link prediction, influence maximization, and block modelling in complex networks Explores new areas such as sarcasm and sentiment analysis, block modelling in dynamic networks using stochastic approaches, and behavior modelling of social network users Features novel research topics such as social network user delurking, and threat assessment in social engineering Includes supplementary material: sn.pub/extras

Inhalt

1. The Perceived Assortativity of Social Networks: Methodological Problems and Solutions.- 2. A Parametric Study to Construct Time-aware Social Profiles.- 3. A Parametric Study to Construct Time-aware Social Profiles.- 4. The DEvOTION Algorithm for Delurking in Social Networks.- 5. Social Engineering Threat Assessment using a Multi-layered Graph-based Model.- 6. Through The Grapevine: A Comparison of News in Microblogs and Traditional Media.- 7. Prediction of Elevated Activity in Online Social Media Using Aggregated and Individualized Models.- 8. Unsupervised Link Prediction Based on Time Frames in Weighted-Directed Citation Networks.- 9. An Approach to Maximize the Influence Spread in Social Networks.- 10. Energy Efficiency Analysis of the Very Fast Decision Tree Algorithm.