Mining Complex Networks

3 Credits · Level: Graduate, Undergraduate

Networks (or graphs) are ubiquitous. Social networks, information networks, and technological networks can easily be modeled as graph structures. This course presents state-of-the-art methods and algorithms for exploring, analyzing, and mining large-scale networks. We will investigate different approaches for extracting meaningful information from large-scale graph data in efficient ways, and see their application in different domains. Topics will include graph modeling, centrality analysis, community detection, subgraph mining/motif discovery, random walk models, event detection, and link prediction. We will implement different algorithms studied in class to better understand the properties of real-world graphs.