Encyclopedia of Big Data Technologies

2019 Edition
| Editors: Sherif Sakr, Albert Y. Zomaya

Feature Learning from Social Graphs

  • Vineeth RakeshEmail author
  • Lei TangEmail author
  • Huan LiuEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-3-319-77525-8_273

Overview

The rapid evolution of social network platforms such as Twitter, Facebook, and Instagram has resulted in heterogeneous networks with complex social interactions. Despite providing a rich source of information, the dimensionality of features in these networks poses several challenges to machine learning tasks such as personalization, prediction, and recommendation. Therefore, it is important to ask the question “how to capture such complex interactions between users in simplified dimensions?”. To answer this question, this entry explores network representation learning (NRL), where the objective is to model the complex high-dimensional interactions between nodes in a reduced feature space while simultaneously capturing the neighborhood similarity and community membership.

Information in the modern world flows in the form of graphs such as social networks, biological networks, and World Wide Web. These graphical structures reveal many intriguing characteristics about the...
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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Data Mining and Machine Learning Lab, School of Computing, Informatics, and Decision Systems EngineeringArizona State UniversityTempeUSA
  2. 2.Clari Inc.SunnyvaleUSA