Fall 2025, Fall 2023, Fall 2022

This graduate-level course covers state-of-the-art research on Network Science. Networks, also called as graphs, represent connections between objects ranging from webpages to users in social networks, to neurons in our brains. These networks often have millions or even billions of nodes and edges between them. Within this huge interconnected data, how can we extract useful knowledge, understand the underlying processes, and make interesting discoveries? 

This course will cover recent methods and algorithms for exploring and analyzing large-scale networks, as well as applications in various domains (e.g., web, social science, computer networks, neuroscience). Topics include but are not limited to, network core metrics and algorithms,  graph distances, community detection and search on networks, graph embedding, and network representation learning, deep learning on graphs GNN), link prediction and analysis, anomaly detection on graphs, graph summarization, Epidemics, Influence Phenomena on Networks and Social Networks, And applications of networks in the real world.

Topics and Schedule

Spring 2025