Social and Information Networks: Ranking

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Related lectures (32)

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Link-based Ranking: Fundamentals and Algorithms

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

Handling Network Data

Explores handling network data, including types of graphs, real-world network properties, and node importance measurement.

Handling Networks: Graph Theory

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

Statistical analysis of network data

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

Statistical Analysis of Network Data: Structures and Models

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

Handling Networks: Graph Theory

Explores graph theory concepts, centrality measures, and real-world network properties, providing insights into handling diverse types of networks.

Graph Mining: Social Networks Analysis

Explores graph mining in social networks, covering modularity algorithms and community detection.

Link-based ranking: PageRank & HITS

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

Graph Statistics: Random Graphs, Graph Homomorphisms, and Network Analysis

Explores graph statistics, random graph generation, network analysis, centrality measures, and clustering coefficients.

Centrality and Hubs

Delves into centrality and hubs in network neuroscience, exploring node importance, small-world networks, brain structural connectome, and percolation theory.