Google's TensorFlow team recently announced the release of TensorFlow GNN 1.0 (TF-GNN), the latest update to its popular machine learning framework, focusing on the development and expansion of Graph Neural Networks (GNN). This new version provides more powerful tools for complex network analysis, which can be widely applied in various fields such as transportation networks and social networks. The core advantage of TF-GNN lies in its ability to simultaneously handle both the structural information and node features of a graph, effectively bridging the gap between discrete graph data and continuous neural network models. This feature allows developers to delve deeper into the potential correlations in network data, providing strong support for more refined predictions and analysis. In this update, the TensorFlow ecosystem introduces a series of innovative features, with the most notable being the tfgnn.GraphTensor object. This object can represent heterogeneous graphs containing multiple node and edge types, greatly improving the efficiency of graph data processing. This integration not only enhances the TensorFlow ecosystem's ability to handle complex network structures but also provides developers with more flexible and efficient tools. Furthermore, TF-GNN provides a rich Python API that supports subgraph sampling in different computing environments, whether it be a personal workstation or a distributed system. This flexibility is crucial for handling datasets of different scales and complexities, allowing developers to perform efficient computations according to their specific needs. It is worth mentioning that TF-GNN also introduces integrated gradients for model attribution. This feature helps developers gain a deeper understanding of the most influential features in prediction results, optimizing the model training and evaluation process. By combining the structure and data of a graph, GNN can achieve precise predictions for the entire graph, individual nodes, or potential edges, further enhancing the understanding of complex relationships and attributes. As an important component of the TensorFlow ecosystem, TensorFlow GNN 1.0 is now available online with abundant resources, documentation, and code examples, empowering developers to quickly get started and fully utilize this powerful tool.