The rapid rise of digital platforms has transformed communication and information sharing. As social networks become increasingly integral to modern society, social media platforms are motivated to implement algorithms that both enhance user experience and bolster advertising. Yet, the intricate nature of social networks poses significant algorithmic design challenges: How can network data be used to predict node attributes? Which graph representations contain the best prediction power? Of paramount concern is the potential for these algorithms to reinforce biases against marginalized groups.
Social networks often mirror societal biases tied to gender, race, socioeconomic status, and other factors. Algorithms that unintentionally enhance these biases can detrimentally affect individuals and broader communities. Recognizing these implications, this dissertation delves into four projects, each addressing distinct aspects of these challenges. Through our investigations, we propose innovative solutions aimed at bolstering the fairness, accuracy, and predictive prowess of social network algorithms.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/zvk9-w946 |
| Date | January 2023 |
| Creators | Zhang, Yiguang |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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