The recent advancements in the field of graph-based techniques and their applications have seen a significant shift towards more flexible, dynamic, and interpretable approaches. Researchers are increasingly focusing on integrating self-supervised learning, multi-view and multi-scale techniques, and advanced deep learning architectures with explainable AI to enhance performance and transparency. These innovations are not only improving the accuracy and efficiency of existing methods but also opening new avenues for research and application across diverse domains such as software defect prediction, time-series anomaly detection, and multi-robot systems. Notably, the integration of graph neural networks (GNNs) with reinforcement learning for tasks like chess and motor learning has showcased promising generalization abilities and faster learning rates. Additionally, the use of GNNs in real-time anomaly detection frameworks like LogSHIELD has significantly improved detection latency and accuracy. These developments are crucial for applications in cybersecurity, traffic analysis, and industrial monitoring, where timely and accurate predictions are essential. Overall, the field is progressing towards more robust, flexible, and interpretable models that can better capture the intricacies of graph-structured data, paving the way for more sophisticated and scalable solutions in various research areas.