Lectures related to Random Walker Model: PageRank

Link-based Ranking: Fundamentals and Algorithms

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

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.

Link-Based Ranking: PageRank

Explores link-based ranking with a focus on PageRank algorithm and its practical application in web search engines.

Fixed Points in Graph Theory

Focuses on fixed points in graph theory and their implications in algorithms and analysis.

Influence: Social Interaction and Online Metrics

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

Graph Algorithms II: Traversal and Paths

Explores graph traversal methods, spanning trees, and shortest paths using BFS and DFS.

Social and Information Networks: Ranking

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

Markov Chains: PageRank Algorithm

Explores the PageRank algorithm within Markov chains, emphasizing ergodicity and convergence for web page ranking.

Shortest Path Algorithms: BFS and Dijkstra

Explores Breadth-First Search and Dijkstra's algorithm for finding shortest paths in graphs.

Digital History: Exploring 20th Century through Press Analysis

Explores digital history and press analysis, emphasizing the impact of digital tools on knowledge dissemination and historical research.

Graph Algorithms: Ford-Fulkerson and Strongly Connected Components

Discusses the Ford-Fulkerson method and strongly connected components in graph algorithms.

Vector Space Information Retrieval

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

Graph Theory and Network Flows

Introduces graph theory, network flows, and flow conservation laws with practical examples and theorems.

Statistical analysis of network data

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

Stein Algorithm: Polynomial Identity Testing

Explores the Stein algorithm for polynomial identity testing and the minimization of a cut problem.

Handling Network Data

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

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

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

Algorithmic Paradigms for Dynamic Graph Problems

Covers algorithmic paradigms for dynamic graph problems, including dynamic connectivity, expander decomposition, and local clustering, breaking barriers in k-vertex connectivity problems.

Graph Mining: Social Networks Analysis

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