Link-based ranking: PageRank & HITS

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Introduces the basics of graph algorithms, covering traversal, representation, and data structures for BFS and DFS.

Explores link-based ranking, document classification, and graph mining techniques.

Discusses social influence, online metrics, Page Rank, and the impact of money.

Introduces elementary graph algorithms, focusing on Breadth-First Search and Depth-First Search.

Introduces network data structures, models, and analysis techniques, emphasizing permutation invariance and Erdős-Rényi networks.

Explores vector space retrieval, covering computation, examples, limitations of boolean retrieval, and query weights.

Explores expanders, Ramanujan graphs, eigenvalues, Laplacian matrices, and spectral properties.

Explores interlacing families, Ramanujan graphs, and their construction using signed adjacency matrices.

Explores the role of graphs in deep learning, focusing on their structure, applications, and techniques for processing graph data.

Explores statistical analysis of network data, covering graph structures, models, statistics, and sampling methods.

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