## 2 Literature Review  
An industrial ecosystem is defined as the various subjects within a specific industry (business, organizations, consumers, governments, etc.) and the interaction network formed by them. It is described as a complex system that promotes innovation and sustainability through cooperation and competition between the subjects [8]. To establish a successful ecosystem, it is important to understand the interdependence among cooperators and effectively monitor business cooperation based on this understanding [9]. Input-Output (I/O) analysis is useful for identifying such interdependencies by showing how outputs from one industry serve as inputs for another. It helps reveal the structure of cooperation within an ecosystem and assess the economic ripple effects of each sector. [PERSON] (1955) provided one of the earliest and most comprehensive reviews of input-output analysis, laying the theoretical foundation for understanding inter-industry relationships through quantitative models [10]. [PERSON] (1992) extended the application of I/O analysis to industrial ecology, demonstrating how economic activities across industries are linked to environmental impacts, particularly in terms of resource use and emissions [11]. [PERSON] et al. (2005) applied I/O analysis to Korea's maritime industry to evaluate its forward and backward linkages, illustrating how a single industry interacts with others and contributes to the national economy [12]. [PERSON] et al. (2011) analyzed China's ICT industry using I/O data to measure the degree of industrial integration and convergence, highlighting how structural changes in one sector influence the broader ecosystem [13]. The SCOR (Supply Chain Operations Reference model) research group argued 25 years ago that more attention should be paid to processes rather than objects [14]. In practice, research using network analysis is predominant in the field of industrial ecosystem studies [15; 16; 17; 18; 19]. In particular, network analysis is a key tool in understanding relationships and structural patterns in social sciences [20; 21; 22]. The node centrality indexes, such as degree centrality, betweenness centrality, and closeness centrality, have frequently been used to understand network patterns. [PERSON] and [PERSON] (2007) discovered, using degree centrality and betweenness centrality in network analysis, that psychological and behavioral characteristics, such as obesity, smoking, and happiness, tend to be contagious [23]. There is also research that assesses network connectivity and information paths in the IT industry using network density, clustering coefficients, and information centrality, arguing that the role of hub businesses in the platform ecosystem contributes to the success of the entire ecosystem [24]. Furthermore, there is a case explaining that a node (a hospital) with a high frequency of cooperation in a hospital network, identified through betweenness centrality, could improve the quality of patient treatment; and that a patient network could provide a path through which diseases are spread and establish strategies for blocking it [25]. The closeness centrality index, which disseminates information in emergencies, is used in the safety management field [26]. The role and importance of major nodes (businesses) were assessed by modeling trade relationships between major businesses in the semiconductor equipment industry supply chain into a network, using degree centrality and betweenness centrality [27]. It was argued, through social network analysis, that promoting business relationships between various enterprises is crucial, as the inter-enterprise network structure plays an important role in the sustainability and resilience of the local industrial ecosystem [28]. However, research focused on node centrality fails to sufficiently reflect the overall network structure or the characteristics of interdependence when assessing the importance of nodes from a localized perspective [29].