Module Catalogues, Xi'an Jiaotong-Liverpool University      Module Code: CSE413 Module Title: Social Network Analysis Module Level: Level 4 Module Credits: 5.00 Academic Year: 2019/20 Semester: SEM2 Originating Department: Computer Science and Software Engineering Pre-requisites: N/A     Aims To study, review and discuss methods, models and algorithms for Social Network Analysis (SNA) with a focus on applications of these methods using relevant examples. Learning outcomes  A. Demonstrate a critical and broad understanding of Social Network Analysis (SNA) with a series of well-defined concepts, models, algorithms, and applications. B. Explain the strength and weaknesses of different social network models and algorithms. C. Adapt or combine some of the key elements of existing SNA models and mechanisms to design SNA solution to a real-world application problem. D. Devise a social network computer program for the real-world application. E. Implement and/or develop key algorithms of SNA. Method of teaching and learning  Students are expected to attend two hours of formal lecture and two hours of lab in a typical week. Lectures introduce students to academic and theoretical contents while labs are to allow students to put the knowledge into practice. In addition, students are expected to devote unsupervised time to private study. Private study will provide time for reflection and consideration of lecture material and background reading. Syllabus  • Introduction, Overview, and History of SNA • Graphs and Matrices • Nodes, Ties and Influences • Personal Networks • Community Detection and Evaluation • Link Analysis • Proximity Measures • Graph Cluster Analysis • Whole Social Network Analysis • Program and Tools for SNA: R Programming, Python iGraph Delivery Hours   Lectures Seminars Tutorials Lab/Prcaticals Fieldwork / Placement Other(Private study) Total Hours/Semester 26       26    98  150

Assessment

Sequence	Method	% of Final Mark
1	Assessment Task1	15.00
2	Assessment Task2	15.00
3	Project	70.00

Module Catalogue generated from SITS	CUT-OFF: 12/14/2019 3:32:24 PM