Graph Mining: Social Networks Analysis

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Related lectures (32)

Graph Mining: Social Networks and Data Analysis

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

Link-based Ranking: Fundamentals and Algorithms

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

Data Mining: Introduction

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

Network Analysis: Methods and Applications

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

Social and Information Networks: Ranking

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

Graph Mining: Modularity and Community Detection

Explores community detection in graphs using modularity and edge betweenness.

Statistical analysis of network data

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

Social and Information Networks: Structure

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

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.

Handling Networks: Graph Theory

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

Digital History Projects: Design and Analysis

Covers digital history projects, social relationships in research, data preprocessing challenges, and network analysis tools.

Handling Network Data

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

Modularity: Community Detection Algorithms

Explores the Louvain Modularity Algorithm for optimizing community quality through modularity and its applications in social network analysis.

Introduction to Applied Data Analysis

Introduces the Applied Data Analysis course at EPFL, covering a broad range of data analysis topics and emphasizing continuous learning in data science.

Social Network Analysis: Modularity Measure

Explores the computation of the modularity measure and betweenness centrality in graphs for community detection.

Graph Mining: Link Based Ranking and Document Classification

Explores link-based ranking, document classification, and graph mining techniques.

Graph Coloring: Theory and Applications

Explores graph coloring theory, spectral clustering, community detection, and network structures.

Network Formation: Random Connection Models

Explores network formation through random connection models and node degree distribution.

Unsupervised Learning: Dimensionality Reduction and Clustering

Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.

Social Network Analysis: Friendship Relations

Introduces social network analysis through Python code modeling friendship relations.

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