Journal of Innovations in Computer Science and Engineering (JICSE)

Abstract Understanding dynamic systems like disease outbreaks, social influence, and information diffusion requires effective modeling of complex networks. Traditional evaluation methods for static networks often fall short when applied to temporal networks. This paper introduces DGSP-GCN (Dynamic Graph Similarity Prediction based on Graph Convolutional Network), a deep learning-based framework that integrates graph convolutional networks with dynamic graph signal processing techniques to provide a unified solution for evaluating generative models and detecting anomalies in dynamic networks. DGSP-GCN assesses how well a generated network snapshot matches the expected temporal evolution, incorporating an attention mechanism to improve embedding quality and capture dynamic structural changes. The approach was tested on five real-world datasets: WikiMath, Chickenpox, PedalMe, MontevideoBus, and MetraLa. Results show that DGSP-GCN outperforms baseline methods, such as time series regression and random similarity assignment, achieving the lowest error rates (MSE of 0.0645, MAE of 0.1781, RMSE of 0.2507). These findings highlight DGSP-GCN's effectiveness in evaluating and detecting anomalies in dynamic networks, offering valuable insights for network evolution and anomaly detection research.

Abstract 3D object detection for self-driving cars has emerged as a critical challenge, largely due to the constraints of computing resources and the requirement for real-time processing. For evaluation, we utilize the KITTI benchmark dataset, which is widely used for self-driving cars and 3D object detection research. In this paper, an adaptive Weighted knowledge distillation approach is proposed to enhance detection accuracy while improving computational efficiency. Based on the performance of the teacher model, point clouds are divided into TPS (Teacher Performs Strongly) and TPW (Teacher Performs Weakly). The student model adapts its learning strategy dynamically: for TPS point clouds, it closely imitates the teacher by increasing the distillation weight, whereas for TPW point clouds, it prioritizes learning from raw data, reducing reliance on the teacher’s guidance. Additionally, a data pruning mechanism creates a smaller dataset from the KITTI benchmark based on the teacher model’s performance, while maintaining the TPS-TPW ratio. Experimental results indicate that the student model achieves comparable, and in some cases superior, performance to the teacher model. Specifically, it enhances recall by up to 1.5% and precision by up to 2.2% in complex scenarios. The student model is a lightweight network that learns from the teacher through knowledge distillation [28] and is solely used during execution. This design reduces execution time by nearly 50% while maintaining high detection accuracy. These findings emphasize the effectiveness of the proposed framework in real-time 3D object detection, making it well-suited for deployment in resource-constrained environments such as self-driving cars.