Graph Neural Network (GNN)

Alignment training optimizes the model's statistical behavior in known scenarios rather than establishing formally verifiable security boundaries, and may fail when faced with rare instruction combinations or role-playing frameworks outside the training distribution.

AlphaFold2 elevated the prediction accuracy of protein monomer structures to near-experimental levels by incorporating attention mechanisms (the Evoformer module) and equivariant neural networks

Graph Neural Networks (GNNs) bind the topological structure of a graph together with the linear transformation of feature vectors.

Supervised graph neural networks perform well on traffic forecasting tasks, but they are often trained for a specific dataset and sensor graph, making deployment to new road networks or dynamically changing networks costly.

Compared with the point-by-point message passing of traditional graph neural networks, Transolver can capture long-range dependencies on large-scale geometries more efficiently while remaining robust to mesh resolution.

In power load forecasting, the GNN simultaneously performs cross-series parameter sharing and neighbor-series reading, and the model's improvement over non-graph models may stem mainly from parameter sharing rather than neighbor reading.

Traditional particle physics reconstruction relies on algorithms such as Kalman filtering and combinatorial optimization, which are extremely compute-intensive; in recent years graph neural networks and Transformers have been used for track finding and particle flow reconstruction.

Topology-sensitive models refer to machine learning models that explicitly encode graph structure information (such as adjacency matrices, node degrees, and graph Laplacian eigenvalues) in the input features.

In MARL, each agent is typically treated as a node and observable interaction relationships as edges, with a GNN aggregating neighbor information at each time step to generate policy inputs for each agent.

The limitation of GNNs is that the graph structure is often given by heuristic rules; if the true interaction relationships are unobservable, the GNN can only infer them implicitly, and its effectiveness depends on the accuracy of the interaction graph estimation.

The message-passing mechanism of graph neural networks naturally encodes how uncertainty propagates along the physical network.

Traditional machine learning potentials and graph neural networks are usually trained for specific tasks, have limited generalization ability, and have limited extrapolation ability for structures outside the training distribution.

Graph neural networks (GNN) treat crystal structures as graphs and aggregate neighbor information through message passing to predict properties such as formation energy, band gap and elastic modulus, with typical models including CGCNN, SchNet, MEGNet and ALIGNN.

In the class-incremental graph learning scenario of graph neural networks, as new classes arrive one after another the model exhibits catastrophic forgetting, and predictions are often dominated by the most recently learned classes, producing recency bias.

Among molecular representation methods, SMILES strings are compact and can be directly fed to language models, but they are not unique and are insensitive to subtle structural changes; molecular fingerprints (such as ECFP/Morgan fingerprints) are suited to similarity search and traditional machine learning but lose topological details; graph neural networks treat atoms as nodes and chemical bonds as edges and are the current mainstream approach for molecular property prediction and generation.

Part 3 graph machine learning covers traditional methods and feature engineering on graphs, shallow node embeddings, knowledge graph embeddings, graph neural networks, and subgraph sampling.

GNNs address the problems in graph data of a non-fixed number of neighbors and meaningless ordering through the message passing mechanism: in each layer, a node first aggregates the feature information of its neighbors, then combines it with its own representation and updates it through a nonlinear transformation.

The message-passing mechanism originates from the graph neural network (GNN) field and refers to an iterative mechanism in which nodes encode information, pass it to neighboring nodes, and then aggregate and update.

GridSFM models the power grid as a graph, treating buses as vertices and transmission lines as edges, using an attention module to handle grid structure, and possesses topology-agnostic capability.

Graph neural network-based methods have been used for the screening of novel antibiotic candidate molecules.