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This paper discusses the issues of industrial cluster analysis. Initially, the authors explore theoretical approaches to understanding the clusters phenomenon and their identification and analysis. Looking at industrial clusters as network structures connected by various forms of interaction between members, such as ownership linkages, transactions, the presence of common counterparts, and participation in arbitration processes, the authors propose visualizing clusters using social network analysis metrics. This approach helps to address one of the main difficulties when contacting the members of industrial clusters for a subsequent survey or in-depth interviewing. The analysis concludes with a discussion of the proposed method as a way to identify cluster members and determine the most significant ones that are the primary nodes of the network. These key members usually possess enough relevant information about the structure, coordination mechanisms, general strategy, and cluster management system. Therefore, it is possible to limit the list of interviewed respondents without a substantial loss in empirical data quality. The case of the textile industry cluster presented in this paper confirms the applicability of social network analysis to the visualization and description of industrial clusters.

Application of Social Network Analysis to Visualization and Description of Industrial Clusters: A Case of the Textile Industry