<![CDATA[Graph coloring is an important topic in graph theory with applications in scheduling, traffic management, network optimization, and other conflict-avoidance problems. This study aims to compare the performance of the Greedy, Welch–Powell, and Sequential algorithms in terms of computational time and the number of colors used across graphs with different sizes and structures. A quantitative comparative approach was employed using graph datasets obtained from open sources. The datasets were represented as adjacency matrices and processed using the three graph coloring algorithms under a consistent computational procedure. The results show that the Greedy and Welch–Powell algorithms produced the same number of colors for most datasets, while the Greedy algorithm generally required the least computational time. For larger graphs, the Greedy and Welch–Powell algorithms produced fewer colors than the Sequential algorithm in some cases. The Sequential algorithm demonstrated relatively fast execution but tended to require more colors, whereas the Welch–Powell algorithm achieved competitive coloring results with greater computational time. These findings imply that algorithm selection should consider both coloring quality and computational efficiency. The comparative framework also has educational implications by supporting algorithmic thinking, computational thinking, and evidence-based problem-solving in graph theory learning.]]>
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