Machine Learning Techniques for Online Social Networks

The book covers tools in the study of online social networks such as machine learning techniques, clustering, and deep learning. A variety of theoretical aspects, application domains, and case studies for analyzing social network data are covered. The aim is to provide new perspectives on utilizing...

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Médium: Elektronický zdroj E-kniha
Jazyk:English
Vydavateľské údaje: Cham : Springer International Publishing, 2018.
Vydanie:1st ed. 2018.
Edícia:Lecture Notes in Social Networks,
Predmet:
Social sciences-Data processing.
Social sciences-Computer programs.
Data mining.
Social media.
Artificial intelligence.
ISBN:9783319899329
ISSN:2190-5428
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Obsah:
  • Chapter1. Acceleration of Functional Cluster Extraction and Analysis of Cluster Affinity
  • Chapter2. Delta-Hyperbolicity and the Core-Periphery Structure in Graphs
  • Chapter3. A Framework for OSN Performance Evaluation Studies
  • Chapter4. On The Problem of Multi-Staged Impression Allocation in Online Social Networks
  • Chapter5. Order-of-Magnitude Popularity Estimation of Pirated Content
  • Chapter6. Learning What to Share in Online Social Networks using Deep Reinforcement Learning
  • Chapter7. Centrality and Community Scoring Functions in Incomplete Networks: Their Sensitivity, Robustness and Reliability
  • Chapter8. Ameliorating Search Results Recommendation System based on K-means Clustering Algorithm and Distance Measurements
  • Chapter9. Dynamics of large scale networks following a merger
  • Chapter10. Cloud Assisted Personal Online Social Network
  • Chapter11. Text-Based Analysis of Emotion by Considering Tweets.

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