Graph Algorithms for Network Analysis
Master essential graph algorithms through practical applications in this 6-week course. Learn shortest paths, spanning trees, and network flows.
Master essential graph algorithms through practical applications in this 6-week course. Learn shortest paths, spanning trees, and network flows.
This comprehensive course explores graph algorithms and their real-world applications in navigation systems, social networks, and computer networks. Students learn to implement various graph algorithms, including shortest path algorithms (Breadth-First Search, Dijkstra's, and Bellman-Ford), minimum spanning trees (Kruskal's and Prim's algorithms), and network flow analysis. The course combines theoretical foundations with practical programming assignments, allowing students to solve real-world problems like optimizing road networks, analyzing social networks, and planning computer network infrastructure. Through hands-on projects, participants develop efficient algorithmic solutions for complex network-based challenges.
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What you'll learn
Master graph exploration and decomposition techniques
Implement efficient shortest path algorithms like Dijkstra's and Bellman-Ford
Design minimum spanning tree algorithms for network optimization
Apply network flow algorithms to solve transportation problems
Analyze real-world networks using graph algorithms
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, Exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 5 modules in this course
The course covers graph algorithms essential for analyzing and optimizing networks. Students learn graph representation and decomposition, shortest path algorithms, minimum spanning trees, and network flows. The curriculum includes practical applications in road networks, social networks, and computer networks, with programming assignments that reinforce theoretical concepts through real-world problem-solving.
Graph Decomposition - Part 1
Module 1
Graph Decomposition - Part 2
Module 2
Shortest Paths in Graphs
Module 3
Minimum Spanning Trees
Module 4
Flows in Networks
Module 5
Fee Structure
Instructors
8 Courses
Mathematics and Theoretical Computer Science Prodigy
Daniel Mertz Kane serves as a full professor with a joint appointment in Mathematics and Computer Science at the University of California, San Diego, where he has established himself as a leading researcher in theoretical computer science, combinatorics, and number theory. Born in 1986 to academic parents in Madison, Wisconsin, his extraordinary mathematical talent emerged early, mastering K-9 mathematics by third grade and conducting university-level research under Ken Ono while still in high school. His academic achievements include two gold medals in the International Mathematical Olympiad (2002, 2003), being one of only eight people in history to become a four-time Putnam Fellow, and winning the 2007 Morgan Prize. After earning dual bachelor's degrees from MIT in mathematics with computer science and physics (2007), he completed his Ph.D. at Harvard under Barry Mazur in 2011. His research contributions span multiple areas, including groundbreaking work in computational statistics, Boolean functions, and machine learning, earning him numerous awards including the IBM Pat Goldberg Memorial and PODS best paper awards. He recently co-authored a book on robust statistics with Ilias Diakonikolas, to be published by Cambridge University Press
13 Courses
Distinguished Computer Scientist and Algorithms Expert
Alexander S. Kulikov serves as a visiting professor at the University of California, San Diego, and a leading research fellow at the Steklov Institute of Mathematics in St. Petersburg. His academic journey includes earning his Ph.D. in 2009 and Dr.Sci. in 2017 from the St. Petersburg Department of Steklov Institute of Mathematics. His research focuses on algorithms for NP-hard problems and circuit complexity, with significant contributions to computational complexity theory and algorithm design. He has authored several influential educational resources, including "Learning Algorithms Through Programming and Puzzle Solving" and co-created major online courses on platforms like Coursera and edX. His teaching experience spans more than eight years, during which he has developed innovative approaches to algorithms education. Currently at JetBrains as a researcher, he continues to advance the field through his work on algorithmic problem-solving and computational complexity, while maintaining his academic connections through his visiting professorship at UCSD and research position at Steklov Institute.
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