## 2 Literature Review
### Relationship Strength Quantification Methods  
In eye-tracking research, quantifying the relationships between Areas of Interest (AOIs) is essential for understanding users' visual behavior. With advancements in research, effectively handling AOI transition regions and accurately quantifying relationship strength have become critical issues in static scene analysis. Early studies primarily quantifiedrelationship strength based on the distance between AOIs or the frequency of fixation point transitions. For example, [PERSON] et al. [82] proposed the Area Gap Tolerance (AGT) method, which evaluates the association between AOIs by setting a fixed tolerance range. Although this method is intuitive, it struggles to accurately capture transitional relationships between AOIs in dense AOI distributions or complex visual scenes. Additionally, it does not account for the dynamic distribution of visual attention, and thus, potentially overlooks subtle visual flow patterns between AOIs. [PERSON] et al. [83] introduced an analysis framework based on a buffering mechanism, specifically addressing the handling of overlapping AOI regions. They found that adaptive buffer zones enhance AOI relationship identification. However, this method faces computational efficiency challenges, particularly when processing large-scale data, as buffer zone settings may increase computational costs. Therefore, improving computational efficiency while maintaining accuracy remains a key research challenge. [PERSON] and [PERSON] [84] introduced a graph-based analytical framework, treating AOIs as nodes and measuring relationship strength through fixation transition frequencies. While this method captures the flow patterns of visual attention, it focuses on node connections and does not directly consider the spatial distribution of AOIs. Instead of a graph-based method, [PERSON] et al. [85] proposed the Point-of-Interest (POI) approach, which describes visual attention distribution using a gradual transition method, overcoming the rigid boundary constraints of traditional AOI methods. However, when fixation point distributions are scattered, this method may fail to accurately capture visual attention flow relationships. Regarding the representation of relationship strength, [PERSON] et al. [86] explored the distribution characteristics of visual attention from a statistical modeling perspective and used numerical calculations to estimate relationship strength. However, they did not provide an intuitive visualization tool to illustrate the strength of relationships between AOIs, making it challenging to interpret results in applied contexts. Although these studies provide an important foundation for AOI analysis, several key challenges remain in relationship strength quantification, including how to adaptively adjust the analysis range based on different stimulus scenes, how to integrate spatial and temporal dimensions into relationship strength representations, and how to provide more intuitive visual representations. Particularly in dynamic scenes, accurately capturing the flow of visual attention between AOIs while considering the influence of temporal variations remains a pressing issue. From a cognitive perspective, the visual connections between AOIs reflect more than spatial adjacency or transition frequency--they represent the viewer's exploratory strategies and the internal structure of attentional shifts. According to [PERSON], [PERSON], and [PERSON] [87], the precision of guiding features plays a central role in visual search by shaping saccadic targeting and influencing the allocation of attention. By quantifying these inter-AOI relationships, researchers can infer how viewers navigate scenes, prioritize diagnostic information, and construct task-relevant visual pathways. To address these challenges, this study focuses not only on the fixation characteristics of individual AOIs but also on the visual relationship strength between AOIs.