Cost Analysis of Euclidean and Manhattan Distances in Node Networks

Author(s)	Ms. S Yoga, Ms. M Abarna
Country	India
Abstract	This Study compares the Euclidean and Manhattan distance metric in a network of nodes by calculating the cost of assignment using the Hungarian method. The Hungarian method is then applied to determine the optimal assignment and total cost for each distance metric. The results highlight the differences in total cost between Euclidean and Manhattan distances, providing insights into how distance measures affect optimization in network problems. The results clearly demonstrate that the Euclidean distance metric leads to a significantly lower optimal cost compared to the Manhattan metric. Since Euclidean distance reflects the true shortest path between nodes, it ensures higher cost efficiency in the assignment process.
Keywords	Euclidean distance, Manhattan distance, Networks, Hungarian Method
Field	Mathematics
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-03-11

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