Graph Mining: Social Networks Analysis

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Related lectures (32)

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Handling Network Data

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

Inference for Stochastic Processes: Large Networks Analysis

Explores inference for stochastic processes, emphasizing large networks analysis and the need for new theories and methods.

Dynamic Communication Networks: Structures and Predictability

Explores fundamental structures of dynamic communication networks, including predictability in human mobility and network communities.

Data Analysis to AI and ML, Social Media

Explores the evolution from data analysis to AI and ML, emphasizing big data, machine learning, and social media interaction.

Big Data Best Practices and Guidelines

Covers best practices and guidelines for big data, including data lakes, architecture, challenges, and technologies like Hadoop and Hive.

Urban Metabolism Analysis

Explores systemic environmental assessment, national material flow analysis, and urban metabolism dashboard development for Zurich using open data.

Understanding Big Data in Neuroscience

Delves into Big Data in neuroscience, analyzing large datasets and addressing challenges in data organization, standardization, integration, and visualization.

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

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

Distributed Information Systems: Course Overview

Introduces the Distributed Information Systems course, emphasizing key concepts and practical tools for data processing.

Digital History and Digitized Press

Delves into the 'digital turn' in history, examining historical research using digitized newspapers and exploring text reuse, word embeddings, and data visualization.