Graph Mining: Social Networks Analysis

Related lectures (32)

Page 1 of 4

Explores graph mining in social networks, covering data analysis and community detection.

Covers the fundamentals and algorithms of link-based ranking, including anchor text indexing, PageRank, HITS, and practical implementations.

Covers the challenges and opportunities of data mining, practical questions, algorithm components, and applications like shopping basket analysis.

Explores network analysis methods, operationalizing concepts, historical applications, and challenges in treating time within networks.

Explores the significance of ranking in networks, emphasizing algorithms like PageRank and HITS for web page ranking.

Explores community detection in graphs using modularity and edge betweenness.

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

Explores the structure of social and information networks, focusing on giant components, clustering, tie formation, and network connectivity.

Explores link-based ranking through PageRank and HITS algorithms, covering practical examples and challenges in web search and ranking methods.

Covers the fundamentals of handling networks and centrality measures in graph theory.