|Course title||Advanced Topics in Graph Mining
|Course description|| This course introduces advanced techniques for graph mining. Topics to be covered include, but are not limited to graph classification, graph clustering, community detection, influence maximization, dense subgraph finding, frequent subgraph mining, correlated subgraph mining, subgraph matching, subgraph motif enumeration, graph centralities, and other important and emerging topics in graph mining. The course will cover both classic and the state-of-the-art algorithms and systems for the topics to be studied.
|Semester||1 or 2|
|Grade Descriptors||A/A-: EXCELLENT – exceptionally good performance and far exceeding expectation in all or most of the course learning outcomes; demonstration of superior understanding of the subject matter, the ability to analyze problems and apply extensive knowledge, and skillful use of concepts and materials to derive proper solutions.
B+/B/B-: GOOD – good performance in all course learning outcomes and exceeding expectation in some of them; demonstration of good understanding of the subject matter and the ability to use proper concepts and materials to solve most of the problems encountered.
C+/C/C-: FAIR – adequate performance and meeting expectation in all course learning outcomes; demonstration of adequate understanding of the subject matter and the ability to solve simple problems.
D+/D: MARGINAL – performance barely meets the expectation in the essential course learning outcomes; demonstration of partial understanding of the subject matter and the ability to solve simple problems.
F: FAILURE – performance does not meet the expectation in the essential course learning outcomes; demonstration of serious deficiencies and the need to retake the course.
|Learning outcomes||At the end of the course of studies, students will have acquired the ability to
1. master the basic skills and techniques of a broad range of important topics in graph mining;
2. understand the key ideas in the design and implementation of some representative algorithms of each topic introduced in the course;
3. understand the key ideas in the design and implementation of the state-of-the-art systems of each topic introduced in the course;
4. relate the algorithms and systems learnt in the course to real-world applications.
(for reference only)
|Recommended Reading List||1. Graph Mining: Laws, Tools, and Case Studies (Synthesis Lectures on Data Mining and Knowledge Discovery) 1st Edition by Deepayan Chakrabarti and Christos Faloutsos, Morgan & Claypool Publishers; 1st Edition (October 19, 2012)
2. Introduction to Data Mining (2nd Edition) (What’s New in Computer Science) 2nd Edition by Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, and Vipin Kumar, Pearson; 2nd Edition (January 4, 2018)
|CSCIN programme learning outcomes||Course mapping|
|Upon completion of their studies, students will be able to:|
|1. identify, formulate, and solve computer science problems (K/S);|
|2. design, implement, test, and evaluate a computer system, component, or algorithm to meet desired needs (K/S);
|3. receive the broad education necessary to understand the impact of computer science solutions in a global and societal context (K/V);||Y|
|4. communicate effectively (S/V);
|5. succeed in research or industry related to computer science (K/S/V);
|6. have solid knowledge in computer science and engineering, including programming and languages, algorithms, theory, databases, etc. (K/S);||Y|
|7. integrate well into and contribute to the local society and the global community related to computer science (K/S/V);|
|8. practise high standard of professional ethics (V);|
|9. draw on and integrate knowledge from many related areas (K/S/V);
|Remarks: K = Knowledge outcomes; S = Skills outcomes; V = Values and attitude outcomes; T = Teach; P = Practice; M = Measured|