<![CDATA[In recent years, the airline industry has increasingly utilized social media and online platforms to engage customers and enhance brand loyalty. Identifying key influencers within these networks is crucial for optimizing marketing strategies and improving customer engagement. Influencers play a pivotal role in shaping opinions, driving behaviors, and amplifying brand messages within social networks. Consequently, efficient methods for detecting influencers are essential for understanding network dynamics and maintaining a competitive edge. This study introduces a novel contribution to the field of social network analysis by proposing the Leiden Coloring Algorithm, an enhancement of the traditional Leiden algorithm that integrates graph coloring techniques. The scientific contribution of this research lies in improving the precision of community detection and computational performance in large-scale networks. Experimental results on five airline-related datasets demonstrate that the proposed method achieves higher modularity (average 0.9375), faster processing time (average 204.88 seconds), and identifies fewer, more cohesive communities compared to the Louvain Coloring Algorithm. These findings highlight the algorithm's effectiveness in influencer detection and its potential application in community detection, marketing optimization, and strategic decision-making within the airline industry.]]>
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