Description: The DGSD-VNE (DiGitization for resource-aware Subgraph Detection in Virtual Network Embedding) algorithm consists of three main phases: Graphize-VNE, GraphDetect-VNE, and ReverseGraphize-VNE. In the Graphize-VNE phase, the algorithm digitizes the edges of the Network Infrastructure and Virtual Network Requests through two steps: RequestPoint Conversion, which converts VNR edges into a digital format compatible with the infrastructure, and EdgePoint Conversion, which digitizes the infrastructure edges into a set of points. The GraphDetect-VNE phase is divided into two subphases: MidpointGraphing, which uses the K-Nearest Neighbors (KNN) algorithm to reduce the search space by treating infrastructure edges as data points and mapping them to a 2D plane through midpoint calculation, and Constraint Satisfaction and Structure Preservation, which ensures that the VNR is mapped onto the infrastructure while respecting structural and resource constraints. If structure preservation and constraint satisfaction are successful, the edges are added to the Selected list, allocated for a specific duration, and deallocated when the allocation period expires. Finally, the ReverseGraphize-VNE phase converts the digitized, embedded VNR back to its original graph form through ReversePointConversion, ensuring the VNR is restored to its graph-oriented structure for further analysis or processing. This simulation environment deploys a medium-sized network infrastructure consisting of 65 virtual nodes distributed across 15 substrate nodes. These virtual nodes interconnect with other virtual nodes with a link probability from0.5 to 0.9, forming the network infrastructure. Consequently, the total number of edges in this network is 1881. The processing capacity of each network infrastructure node, falls within the range of 60 to 80, following a uniform distribution. Additionally, the Link Capacity of network infrastructure edges falls in the range of 60 to 80. In the case of VNRs, their arrival rate follows an exponential distribution with λ= 0.25 to 0.75 as it is used traditionally. The processing capacity of requested VNR nodes, falls in the range of 40 to 60 following a uniform distribution, the Link Capacity of requested VNR links falls in the range of 40 to 60 and the entire system simulates for 400 VNRs with k = 10 as it gives better acceptance rate. Dependencies: 1. matplotlib 3.9.4 2. networkx 3.2.1 3. numpy 2.0.2 Usage: 1. Run the DGSD_VNE.py file to generate the dataset according to the simulation environment setup. 2. Measure the performance metrics by adjusting the values as needed. Note: The dataset used in this study is generated dynamically by running the Python code itself.