This chapter systematically analyzes the dynamic evolution of tie weights in social networks, focusing on the role of weak and strong ties in network structure and their profound impact on social structure. Starting from the dimension of tie strength, this chapter uses weak tie theory to explain interpersonal interactions in social media platforms such as Weibo and applies strong tie theory to analyze the social bonds of private social platforms such as WeChat. Through empirical research on the correlation between weak ties and network shortcuts, this chapter not only verifies the connection between the two, but also explores the quantitative methods and visualization techniques of weighted social networks. In addition, through case studies of co-code networks and team networks, this chapter further elaborates on the application of weighted network analysis in practical research, providing a new perspective and analytical tool for understanding complex interactions in social networks.

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Strong and Weak Ties in Social Networks

  • Jiang Wu

摘要

This chapter systematically analyzes the dynamic evolution of tie weights in social networks, focusing on the role of weak and strong ties in network structure and their profound impact on social structure. Starting from the dimension of tie strength, this chapter uses weak tie theory to explain interpersonal interactions in social media platforms such as Weibo and applies strong tie theory to analyze the social bonds of private social platforms such as WeChat. Through empirical research on the correlation between weak ties and network shortcuts, this chapter not only verifies the connection between the two, but also explores the quantitative methods and visualization techniques of weighted social networks. In addition, through case studies of co-code networks and team networks, this chapter further elaborates on the application of weighted network analysis in practical research, providing a new perspective and analytical tool for understanding complex interactions in social networks.