Online social networks and media

Evi Pitoura | Thursday, July 11
Professor at the University of Ioannina

There is an abundance of big graph data from a variety of real networks, such as biological, communication, transportation, communication, cooperation, and social networks. In this course, we will study the basic tools for analyzing such large networks with a special focus on social networks.


Part 1: Network structure

How are real networks structured?

We will study (a) basic measurements (degree sequence, clustering coefficient, small world phenomena, motifs, homophily) to characterize them, (b) models (random graphs, preferential attachment, network evolution and the forest fire model) to generate them, (c) community detection (spectral clustering, betweeness centrality, modularity) to understand them.

Part 2: Representation learning on networks

How can network structure be encoded into low-dimensional embeddings?

We will study (a) background on link analysis, (b) random-walk based algorithms, (c) graph neural networks, and (d) applications in link recommendations.

Part 3: Spreading phenomena and information diffusion

How are items (such as viruses, opinions, innovations, news) spread through the network?

We will study different diffusion processes: (a) innovation cascades, (b) virus propagation and network epidemics (c) viral marketing and influence maximization, and (d) opinion models and opinion formation.

Part 4: Responsible network science

In the last part of the course, we will consider the emerging topic of responsibility as it applies to social networks. How can we design network algorithms that do not create filter bubbles, build echo chambers, or lead to polarization?


David Easley and Jon Kleinberg. Networks, Crowds, and Markets:

Reasoning About a Highly Connected World, Cambridge University Press, (2010)

Reza Zafarani, Mohammad Ali Abbasi, Huan Liu. Social Media Mining, Cambridge University Press, (2014)

Albert-Laszlo Barabasi. Network Science, Cambridge University Press, (2016)