Graph Mining: Social Networks Analysis

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Related lectures (32)

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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.