Link-based Ranking: Fundamentals and Algorithms

Related lectures (30)

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Covers the basics of graph algorithms, focusing on modeling and representation of graphs in memory.

Covers indexing techniques, distributed retrieval algorithms, and challenges in large-scale web indexing.

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

Covers stochastic properties, network structures, models, statistics, centrality measures, and sampling methods in network data analysis.

Covers graph algorithms, modeling relationships between objects, and traversal techniques like BFS and DFS.

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

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

Explores the significance of anchor text in link-based ranking and its impact on search results.

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

Covers graph properties, representations, and traversal algorithms using BFS and DFS.