The task of reconstructing indoor floorplans has become an increasingly popular subject, offering substantial benefits across various applications such as interior design, virtual reality, and robotics. Despite the growing interest, existing approaches frequently encounter challenges due to high computational costs and sensitivity to errors in primitive detection. In this article, we introduce PolyGraph, a new computational framework that combines a deep-learning based primitive detection network with an optimization-based reconstruction algorithm to facilitate high-quality reconstruction results. Specifically, we develop a novel guided wall point primitive estimation network capable of generating dense samples along wall boundaries. This network not only retains structural detail but also shows improved robustness in the detection phase. Then, PolyGraph utilizes wall points to establish a graph-based representation, formulating indoor floorplan reconstruction as a subgraph optimization problem. This approach significantly reduces the search space comparing to existing pixel-level optimization approaches. By utilizing "structural weight", we seamlessly integrate the structural information of walls and rooms into graph representations, ensuring high-quality reconstruction results. Experimental results demonstrate PolyGraph's effectiveness and its advantages compared to other optimization-based approaches, showcasing its computational efficiency, and its ability to preserve structural integrity and capture fine details, as quantified by the structure metrics.