Covers algorithmic paradigms for dynamic graph problems, including dynamic connectivity, expander decomposition, and local clustering, breaking barriers in k-vertex connectivity problems.
Covers the fundamentals of graph theory, including vertices, edges, degrees, walks, connected graphs, cycles, and trees, with a focus on the number of edges in a tree.
Delves into centrality and hubs in network neuroscience, exploring node importance, small-world networks, brain structural connectome, and percolation theory.
Covers the basics of brain connectomics, including brain networks, terminology, data schemes, preprocessing, node connectivity, and functional connectome structure.
