W11.1-GUO YING

 1. Summary

Social networks consist of relationships between individuals or organizations, forming a social structure that offers a method to analyze the overall structure and patterns of society. Social network analysis is used to identify patterns, determine influential entities, and examine network dynamics. Despite criticism for overlooking individual agency, it is useful across various research fields including sociology, economics, and psychology. Social networks are typically self-organizing, complex, and exhibit globally consistent patterns. As their scale increases, these patterns become more evident, although conducting global network analysis may be impractical due to the sheer volume of information. Analysis is constrained by computational capacity, ethical norms, and practical limitations. Quality of information outweighs scale, hence analysis is usually tailored to research questions. Social networks can be categorized into micro, meso, and macro levels. Social capital exists within populations positioned advantageously within networks. Information within tightly knit clusters is often redundant, while non-redundant information is typically obtained through contacts in different clusters. Structural holes in networks provide additional informational benefits and are a form of social capital. 

2. Interesting point.

As social networks scale up, patterns become more pronounced. However, conducting global network analysis may face challenges due to the vast amount of data, highlighting the need for innovative methods to effectively analyze large-scale networks.

3. Question

How should we balance the relationship between information quality and data scale in social network analysis?

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