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Online Social Networks and Media
Slides and References

 

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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

Degree distributions. Measuring power-laws. Clustering Coefficient, Effective Diameter, Bow-tie structure, Homophily.

Material:

 

Lecture slides (pptx, pdf)

 

Lecture 3: Network Models

Erdos-Renyi graphs. Configuration Model. Preferential Attachment. Small-world models. Forrest-Fire model. Kronecker 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: Link Analysis Ranking, Absorbing Random Walks.

Web search, PageRank, HITS. Random walks on graphs. Absorbing Random Walks. Opinion diffusion.

Material:


Lecture slides: (pptx, pdf)


Lecture 7: Link Prediction.

Link prediction and link recommendations.

Material:


Lecture slides: (pptx, pdf)


Lecture 8:
Information Cascades, Epidemics, Influence Maximization.


Game theoretic information cascade. Models for epidemic spread. Selecting influencers to maximize spread.

Material:


Lecture slides: (pptx, pdf)


Lecture 9: Network Ties

Strong and Weak ties. Strong Triadic Closure. Networks with Positive and Negative ties. Structural Balance.

Material:


Lecture slides:
(pptx, pdf)


Lecture 10: Team Formation in Social Networks. Recommendation systems

Team formation in Social Networks. Recommendation Systems and Social Recommendations.

Material:


Lecture slides:
(pptx, pdf)


Lecture 11: Mining Social Content.

Using content from online social networks and media to predict stock changes, track earthquakes, and understand news cycles.

Material:


Lecture slides: 
(pptx, pdf)