Journal of Electronic Systems Maleerat Maliyaem 16 2 2026 https://doi.org/10.6025/jes/2026/16/2/98-123 https://www.dline.info/jes/fulltext/v16n2/jesv16n2_3.pdf The increasing complexity of modern electronic systems has created a growing demand for intelligent Electronic Design Automation (EDA) frameworks capable of understanding circuit topology, signal propagation, and component interaction behavior. Traditional machine learning approaches are limited in capturing the non-Euclidean structural relationships inherent in electronic circuits because they operate primarily on independent feature vectors rather than connectivity aware representations. This study presents a graph-based analytical framework for intelligent circuit understanding using a Heterogeneous Graph Attention Message Passing Network (HGAMPN). The proposed framework combines heterogeneous graph representation, Message Passing Neural Networks (MPNNs), Graph Attention Networks (GATs), hierarchical graph pooling, and explainable artificial intelligence mechanisms to model connectivity behavior, signal propagation, component dependencies, and hierarchical design structures within electronic circuits. The dataset employed in this study consists of structured circuit topology data obtained from the Kaggle Electronic Circuit for Machine Learning dataset. The dataset contains component level and connectivitylevel information describing electronic circuits composed of resistors, capacitors, transistors, diodes, LEDs, voltage sources, switches, and sensor elements. Each circuit is represented through structured metadata including component identifiers, component categories, pin level relationships, electrical values, and netbased interconnections. This representation enables the reconstruction of circuit structures as heterogeneous graphs where components and nets are modeled as nodes and electrical interconnections are represented asedges. The proposed framework demonstrates how graph-based learning architectures can capture circuit connectivity patterns, identify dominant signal propagation pathways, and discover hierarchical functional modules within electronic systems. The study further illustrates how explainable graph learning mechanisms can identify salient subgraphs, influential electrical paths, and critical component interactions. The resulting framework provides a strong foundation for intelligent EDA, autonomous circuit understanding, graphbased hardware learning, and explainable AI-driven electronic system analysis.