graphlevel

Graphlevel refers to representations, properties, or tasks that pertain to an entire graph, as opposed to individual nodes or edges. In graph learning, a graph-level representation is a fixed-size embedding produced by aggregating node representations produced by a graph neural network (GNN). This embedding is used for graph classification, regression, or similarity tasks and must be invariant to node ordering and scalable to graphs of varying size.

Common strategies for obtaining graph-level representations include global pooling operations over node features, such as sum, mean, or max pooling. More expressive approaches use attention-based pooling, gating mechanisms, or learnable readout functions. Hierarchical or differentiable pooling methods progressively reduce the graph to a smaller, fixed-size representation. Some models also incorporate graph-level readouts that combine structural descriptors with learned features to capture global information.

Applications include molecular property prediction, protein graphs, social networks, and other domains where the output depends on the whole graph. Graph-level tasks include graph classification, regression of properties, and graph similarity measures. Benchmark datasets often used include MUTAG, PROTEINS, D&D, NCI1, and synthetic graph datasets.

Key challenges include choosing pooling that preserves informative structure, handling graphs of different sizes, and ensuring permutation invariance. Computational efficiency and the risk of information loss with deep pooling layers can also impact the quality of graph-level representations.

See also: graph neural networks, graph pooling, readout functions, graph classification, permutation invariance.