CSC 8741 - Graph Mining

4 Credit Hours
Prerequisites: CSC 4740/6740 Data Mining.
Description
This course covers important graph mining techniques, which are not covered by the existing course CSC 4740/6740 Data Mining or any other existing courses. This course will cover the most important research topics in graph mining including graph generators, proximity measurement, community detection, frequent subgraph mining, influence analysis, and multiplex network analysis. During this course, the students will learn the classic algorithms in graph mining including R-MAT graph generator, PageRank, personalized PageRank, SimRank, spectral clustering, modularity, non-negative matrix factorization, gSpan, influence maximization, and densest subgraph detection. The computational complexity and other properties of the problems are discussed. Fast computing algorithms are also introduced. All students should know the problems and applications in the graph mining research area. Students should only learn basic theoretical formulation/analysis of the methods but also accumulate practical hands-on experience on applying those methods. The students will do assignments, take exams, and finish research projects. The students will give presentations about their research projects by the end of the semester.