Graph-Based Market Segmentation Using a Community Detection Algorithm: a Case Study on the Yelp Dataset

Keywords:
Market Segmentation, Community Detection Algorithms, Greedy Modularity Optimization

Abstract:
Market segmentation based on user-generated data becomes challenging when business entities cannot be reliably grouped using only attributive features, and it is necessary to take patterns of a shared user base into account. This problem is particularly relevant in digital marketing. Relationships between businesses revealed through user behavior may indicate hidden market segments and connect IT-based network analysis methods with practical marketing decisions. In this paper, a graph-based approach is used to construct a business-business network from the Yelp dataset and apply a community detection algorithm. The results indicate that this approach can identify structurally coherent groups of businesses with similar audiences, which may serve as a basis for more precise segmentation, targeting, and competitive environment analysis.

CITATION:

IEEE format

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M. Marković, K. Marković, B. Tešić, “Graph-Based Market Segmentation Using a Community Detection Algorithm: a Case Study on the Yelp Dataset,” in Sinteza 2026 - International Scientific Conference on Information Technology, Computer Science, and Data Science, Belgrade, Singidunum University, Serbia, 2026, pp. 233-239. doi:10.15308/Sinteza-2026-233-239

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Marković, M., Marković, K., Tešić, B. (2026). Graph-Based Market Segmentation Using a Community Detection Algorithm: a Case Study on the Yelp Dataset. Paper presented at Sinteza 2026 - International Scientific Conference on Information Technology, Computer Science, and Data Science. doi:10.15308/Sinteza-2026-233-239

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