CS 294: Social and Information Networks - Theory and Practice


The proliferation and growing importance of networked data and systems in our life, as well as in various scientific disciplines, has sparked a flurry of research in developing tools for modeling and analysis of such data, whether it be in the social, technological or biological settings. This course is based on recent research in the analysis of structure, evolution and dynamics of information of such networks. Topics include probabilistic models for network formation, spectral algorithms, models and algorithms for viral propagation, and applications of sampling and sketching techniques. Prerequisites for the course include a basic knowledge of probability, introductory knowledge of algorithms, graphs and linear algebra. Course work will involve two reaction papers, two to three programming assignments with provided data and a final project.

Course Work



Small world networks experiments and models

Small worlds networks in P2P networks and decentralized search

Models of networks

Random models for networks; preferential attachment, copying models; how to fit models to data;

Long tails

Issues in fitting power-laws; models for heavy tails; power-law vs. lognormal distributions;

Clustering Methods on Networks

Random walk; local random walks; spectral algorithms on networks; modularity; problems with modularity definitions; analysis of community structure;

Cascading behavior and viral propagation

Diffusion models; influence propagation & contagion; how to identify influence based diffusion vs. homophily effects.

Computation on Large Graph Structures

Streaming, semi-streaming models for graphs; graph-processing in the Bulk Synchronous Model

Sampling and surveying networks

Sampling networks to collect structural information; surveying social networks about subpopulations; respondent driven sampling -- using the network to collect data; conducting bucket tests for network effects.

Cooperation and collaboration on networks

Query incentive networks; DARPA challenge; behavioral experiments on networks. The following are other topics. We could think about them depending on how much time we have.


Opinions/Ratings on networks

Signed network; structural balance; dynamics of social balance; models; crowdsourcing;

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