This course will introduce mathematical models of networks, analysis of network structure, and visualization process for real-life network datasets. The course will put a special focus on graph drawing, which contains the algorithmic core for network analysis and visualization, and present how an abstract graph layout can be used to create effective visualizations for real-life networks. The content of this course will draw examples from many applied areas such as social sciences, computational biology, communication networks, VLSI circuits, and software engineering. The course is targeted to students interested in network analysis, as well as in visual analytics of network data. Topics include: Combinatorial analysis of graphs, common graph drawing algorithms, network visualization aesthetics, structural analysis of networks, an overview of network analysis tools and software, visualization of geospatial and dynamic networks, layered visualization of large networks, information propagation on a network, user interactions, and case studies from different practical domains. Weekly hours: 3 Lecture hoursPrerequisite(s): CMPT 384.3; and either of CMPT 360.3 or CMPT 381.3 Note: CMPT 353.3 is recommended. Students with credit for CMPT 824 may not take this course for credit. Costs in addition to tuition apply to this course.
This course will introduce mathematical models of networks, analysis of network structure, and visualization process for real-life network datasets. The course will put a special focus on graph drawing, which contains the algorithmic core for network analysis and visualization, and present how an abstract graph layout can be used to create effective visualizations for real-life networks. The content of this course will draw examples from many applied areas such as social sciences, computational biology, communication networks, VLSI circuits, and software engineering. The course is targeted to students interested in network analysis, as well as in visual analytics of network data. Topics include: Combinatorial analysis of graphs, common graph drawing algorithms, network visualization aesthetics, structural analysis of networks, an overview of network analysis tools and software, visualization of geospatial and dynamic networks, layered visualization of large networks, information propagation on a network, user interactions, and case studies from different practical domains. Weekly hours: 3 Lecture hoursPrerequisite(s): CMPT 384.3; and either of CMPT 360.3 or CMPT 381.3 Note: CMPT 353.3 is recommended. Students with credit for CMPT 824 may not take this course for credit. Costs in addition to tuition apply to this course.
