Lectures related to Mining Social Graphs

Graph Mining: Modularity and Community Detection

Explores community detection in graphs using modularity and edge betweenness.

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

Graph Mining: Link Based Ranking and Document Classification

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

Information Exchange in Digital Era

Delves into the complexities of information exchanges in the digital age.

Social Network Analysis: Modularity Measure

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

Graph Coloring: Theory and Applications

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

Handling Networks: Graph Theory

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

Facebook Research: User Analysis, Motivations, Identity, and Privacy

Covers Facebook research on user analysis, motivations, identity, and privacy.

Social Maximum Pilot Project: Pergasona Tower

Delves into the Social Maximum Pilot Project in Lugano, focusing on transforming the decadent Pergasona Tower into a shared community space.

Graph Algorithms II: Traversal and Paths

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

Fixed Points in Graph Theory

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

Statistical Analysis of Network Data: Structures and Models

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

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.

Epidemic Spreading Models

Covers classical models of epidemic spreading and dynamics on networks with examples.

Modular Programming: Managing Project Complexity

Covers modular programming principles, focusing on managing project complexity through effective code structuring and dependency management.

Handling Networks: Graph Theory

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

Linear Algebra: Matrices and Linear Applications

Covers matrices, linear applications, vector spaces, and bijective functions.

Graph Algorithms: Modeling and Traversal

Covers graph algorithms, modeling relationships between objects, and traversal techniques like BFS and DFS.

Handling Network Data

Covers handling network data, types of graphs, centrality measures, and properties of real-world networks.

Graph Algorithms: Basics

Introduces the basics of graph algorithms, covering traversal, representation, and data structures for BFS and DFS.