Lecture

Social and Information Networks: Structure

Related lectures (32)

Network Analysis: Methods and Applications

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

Clustering: Dimensionality Reduction

Explores clustering and dimensionality reduction techniques in finance to clean and simplify data.

Networks and Brain Networks

Covers the basics of networks, focusing on brain networks, historical breakthroughs, small-world and scale-free network discoveries, and the importance of the human connectome.

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.

Network clustering

Explores network clustering, spectral clustering, k-means algorithm, eigenvalue properties, block model estimation, and structural similarity measurement.

Graph Coloring: Theory and Applications

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

Clustering: Unsupervised Learning

Covers clustering algorithms, evaluation methods, and practical applications in machine learning.

Clusters and Communities

Explores clustering, community detection, K-means, GMM, modularity, and the Louvain method.

Stochastic Blockmodel Estimation

Explores Stochastic Blockmodel estimation, spectral clustering, network modularity, Laplacian matrix, and k-means clustering.

Biclustering: Networks MA448

Explores biclustering in data matrices, identifying coherent behavior patterns and discussing computational methods for analysis.

Graph Models and Brain Connectomics

Explores graph theory in brain connectomics, MRI applications, network analysis relevance, and individual fingerprinting.

Supervised Learning Overview

Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.

Network Formation: Random Connection Models

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

Introduction to Networks and Brain Networks

Introduces the fundamentals of network science, focusing on brain networks and their historical breakthroughs.

Graph Mining: Social Networks and Data Analysis

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

Handling Networks: Graph Theory

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

Centrality and Hubs

Delves into centrality and hubs in network neuroscience, exploring node importance, small-world networks, brain structural connectome, and percolation theory.

Unsupervised Learning: Clustering

Explores unsupervised learning through clustering techniques, algorithms, applications, and challenges in various fields.

Inference for Stochastic Processes: Large Networks Analysis

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

Introduction to Image Classification

Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.