network

Online Social Networks and Media
Slides and References

 

Home

Homework


Slides & References

Reading Material

Resources

 

Lecture 1: Introduction

Introduction to main problems about networks. Basic mathematic concepts

Material:

 

Lecture slides (pptx, pdf).
Introduction to Graph Theory (pptx, pdf) (slides from
Social Media Mining)


Lecture 2: Network Measurements and Models

Degree distributions. Measuring power-laws. Clustering Coefficient, Effective Diameter, Bow-tie structure, Homophily.
Erdos-Renyi
 graphs. Configuration Model. Preferential Attachment. Small-world models. Forrest-Fire model.

Material:

 

Lecture slides (pptx, pdf)


Lecture 3: Link Analysis Ranking

Web search, PageRank, HITS. SALSA. Random walks on graphs.

Material:


Lecture slides: (pptx, pdf)


Lecture 4: Community Detection

Communities in Social Networks, Clustering, Betweeness, Modularity

Material:

 

Lecture slides: (pptx, pdf)


Lecture 5: Graph Partitioning, Densest Subgraph

Graph Partitioning, Spectral Clustering. The Densest Subgraph problem.

Material:

 

Lecture slides: (pptx, pdf)


Lecture 6: Epidemics.


Models for epidemic spread.

Material:


Lecture slides: (pptx, pdf)

Lecture 7: Influence Maximization. Opinion Formation models, Absorbing Random Walks. Positive and Negative ties.

Selecting influencers to maximize spread. Opinion formation models. DeGroot and Friedkin-Jonhsen model. Absorbing Random Walks. Opinion maximization. Networks with Positive and Negative ties. Structural Balance.

Material:



Lecture slides: (pptx, pdf)


Lecture 8: Link Prediction.


Link prediction and link recommendations
. The SimRank algorithm.

Material:


Lecture slides: (pptx, pdf)


Lecture 9: Graph Embeddings

Representation Learning on Graphs. Graph Embeddings. Neural Networks. Graph Neural Networks.

Material:


Lecture slides: 
(pptx, pdf)

Lecture 10: Fairness and Diversity in Social Media

Formal models for fairness. Diversity in Social Media.

Material:

Lecture slides: (pptx, pdf)