𝗔 𝗪𝗮𝗹𝗸 𝗶𝗻 𝗙𝗮𝗰𝗲𝗯𝗼𝗼𝗸: 𝗨𝗻𝗶𝗳𝗼𝗿𝗺 𝗦𝗮𝗺𝗽𝗹𝗶𝗻𝗴 𝗼𝗳 𝗨𝘀𝗲𝗿𝘀 𝗶𝗻 𝗢𝗻𝗹𝗶𝗻𝗲 𝗦𝗼𝗰𝗶𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀
Social networks hold massive amounts of data. You often need small samples of this data to study it. Most methods fail to pick users fairly. They pick popular users too often.
This paper presents a method for uniform sampling. It helps you pick users from a social network without bias.
Key points from the research:
- Standard sampling methods favor high degree nodes.
- Bias leads to wrong conclusions about network behavior.
- This new approach ensures every user has an equal chance to be selected.
- It works on large scale graphs like Facebook.
You need unbiased data to build better algorithms. This method solves a core problem in network science.
Source: https://dev.to/paperium/a-walk-in-facebook-uniform-sampling-of-users-in-online-social-networks-463m
Optional learning community: https://t.me/GyaanSetuAi