Machine learning on graphs (static/dynamic, attributed, undirected/directed, single/ensemble) has emerged as an important research topic that finds applications in many domains including social networks, infrastructure design and maintenance, drug discovery, brain networks, and material design. This course will discuss recent advances in machine learning on graphs including neural network architectures and methods to encode graphs into low-dimensional spaces to facilitate machine learning. Specific topics include random walks, kernels, spectral analysis, generative models, node embedding, subgraph embedding, and graph neural networks.
Special Topics Course
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