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Suhansanu Kumar
- Advisor:
- Prof. Hari Sundaram
- Departments:
- Areas of Expertise:
- Reinforcement learning
- Graph sampling
- Thesis Title:
- Sampling Information from Online Social Networks
- Thesis abstract:
- The past decade has seen public information becoming online. Online social networks (OSNs) are a major source of online information content comprising over two billion users and 90\% of the businesses. This vast information source contains information valuable to researchers such as sociologists, network scientists, and advertisers. The extremely large information repository and the limited access to the information in the form of restricted API (Application Programming Interface) access make it pertinent for the information to be sampled. In this thesis, we explore efficient mechanisms to sample information from these online social networks. First, we consider the link based sampling mechanism to sample content from these online social networks to propose a new attributed sampling framework. Next, we explore the attributed search based sampling mechanism to sample target information. Finally, we develop a unified mechanism that learns samplers suited for any specified user-application.
- Downloads:
Contact information:
skumar56@illinois.edu