用认知模型构建音乐图谱，揭示听众感知的结构规律。

Understanding the structural and cognitive underpinnings of musical compositions remains a key challenge in music theory and computational musicology. While traditional methods focus on harmony and rhythm, cognitive models such as the Implication-Realization (I-R) model and Temporal Gestalt theory offer insight into how listeners perceive and anticipate musical structure. This study presents a graph-based computational approach that operationalizes these models by segmenting melodies into perceptual units and annotating them with I-R patterns. These segments are compared using Dynamic Time Warping and organized into k-nearest neighbors graphs to model intra- and inter-segment relationships. Each segment is represented as a node in the graph, and nodes are further labeled with melodic expectancy values derived from Schellenberg's two-factor I-R model-quantifying pitch proximity and pitch reversal at the segment level. This labeling enables the graphs to encode both structural and cognitive information, reflecting how listeners experience musical tension and resolution. To evaluate the expressiveness of these graphs, we apply the Weisfeiler-Lehman graph kernel to measure similarity between and within compositions. Results reveal statistically significant distinctions between intra- and inter-graph structures. Segment-level analysis via multidimensional scaling confirms that structural similarity at the graph level reflects perceptual similarity at the segment level. Graph2vec embeddings and clustering demonstrate that these representations capture stylistic and structural features that extend beyond composer identity. These findings highlight the potential of graph-based methods as a structured, cognitively informed framework for computational music analysis, enabling a more nuanced understanding of musical structure and style through the lens of listener perception.