Junyuan Lin, assistant professor of mathematics at Loyola Marymount University, will investigate “Spectral Algorithms for Dynamic Social Networks and Knowledge Graphs” thanks to $250,000 in grant funding recently awarded to her by the US National Science Foundation (NSF).
Lin’s two-year research project uses her expertise in computational graph theory, machine learning and data mining. This particular project involves the data mining of large datasets from social media networks (in this case, X, formerly known as Twitter) to more accurately detect correlations of topics, to monitor the evolution of clusters and to provide insights for future social discourse, awareness and policy making. “We know that misinformation can have devastating consequences. My research aims to track, interpret, and predict topic evolution patterns and sentiment changes in online social networks, as well as track, measure, and correlate current and important global events or movements,” said Lin.
Her previous collaborative and award-winning research involving protein interaction networks in the medical field produced a Diffusion State Distance (DSD) metric proven effective in weighted and directed networks, winning first place in the Challenge of Disease Identification Module DREAM 2016. This research involved the development of methods and assays to classify and cluster proteins in different species to provide a basis for future research into disease etiologies and treatment.
For Lin’s current research on dynamic social networks, there is a lack of methods for considering directed and weighted edges for data structure graphing, which are important for addressing the heterogeneous nature of social networks. “Drawing similarities between online social networks and protein interaction networks, we will customize DSD-based spectral graph metrics for heterogeneous social interaction networks and their multilayered knowledge graphs,” explains Lin.
In the past three years, Lin’s collaborative research team has processed a dataset of approximately 700 million tweets from the Internet and was able to determine which topic trends are being discussed the most online. As a result, these preliminary findings have led to the publication of several papers in IEEE Big Data on the topics of COVID-19, QAnon and hate speech. “My hope is to use mathematical models and algorithms to further investigate these trends in hot topics and predict potential new topics born from the trends, such as the magnitude of the COVID-19 pandemic or global hate crimes, ” explains Lin. The aim is to document sentiments around these topics and hopefully the data will be useful for policy makers to understand people’s intentions and possible future actions.”
Lin currently has four LMU students collaborating on this research, including three undergraduate applied mathematics or statistics majors and a second-year graduate student majoring in computer science. “Going forward, I also plan to collaborate with faculty and students from other departments, including computer science, mathematics, and sociology, to provide user-friendly approaches to this knowledge graph framework and spectral algorithms,” she said. “I also hope to create interdisciplinary courses to expose students to research areas in social network data mining.”
While Lin believes that researching and analyzing discourse within dynamic social networks is a long-term proposition, she envisions many applications and is considering studying other types of social networks with human interaction such as podcasts and platforms like Reddit. She is also developing various algorithms to efficiently solve queries when working with large data networks. She wants to ensure that the technology she is using is transparent and can be an open source tool for others to use in a variety of research applications.
“I want to provide more opportunities for LMU students to be involved in my investigative work which generally focuses on building efficient models for large data sets. We then analyze this big data, learn about patterns, and build algorithms with predictive models using machine learning,” Lin said. “While my research can be applied to many fields, it is related to the use of scientific computing and of spectral analysis in complex and large networks.” The grant was awarded in August 2024 by NSF’s Division of Mathematical Sciences. Research proposals evaluated by NSF use two merit evaluation criteria approved by the organization’s board: intellectual merit and the most significant impacts broad, which recognize both the research and the potential impact of the project on society. NSF proposal reviewers may also look at other factors such as diverse approaches to important research and educational questions, the potential for transformative advances in a field, and the capacity to build on a new or promising research area.