Link-based ranking: PageRank & HITS

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (30)

Page 3 of 3

Explores optimal binary search trees to minimize expected search cost and discusses graphs representation using adjacency matrices and lists.

Handling Network Data

Covers handling network data, types of graphs, centrality measures, and properties of real-world networks.

Statistical Analysis of Network Data

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

Vector Space Information Retrieval

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

Networks: Computer Representation

Covers the concept of adjacency matrix and adjacency lists for representing networks.

Irreducible Matrices and Strong Connectivity

Explores irreducible matrices and strong connectivity in networked control systems, emphasizing the importance of adjacency matrices and graph structures.

Graph Algorithms: Modeling and Traversal

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

Expander Graphs: Properties and Eigenvalues

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

Interlacing Families and Ramanujan Graphs

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

Graphs in Deep Learning: Applications and Techniques

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