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Neural network architectures for processing and analyzing graph-structured data via relational message passing.
Graph Neural Networks (GNNs) are neural models that leverage explicit graph structure and relational context to aggregate features across nodes and edges. They are applied to tasks such as node classification, link prediction, and graph classification. GNNs entail model assumptions, scalability challenges, and overfitting trade-offs.
Standard metrics for node or graph classification tasks.
Robust measure under class imbalance for link/node prediction.
Operational metrics to evaluate production deployment.
GNNs for predicting user interactions and community detection.
GNN models for predicting toxicity and binding affinity.
Modeling network topologies as graphs to predict failure risks.
Data modeling: define graph schema and extract features.
Baseline: develop and evaluate a simple architecture (e.g., GCN).
Iteratively test more complex architectures (GAT, GraphSAGE).
Scale: introduce sampling, mini-batching or graph partitioning.
Optimize: refine inference path and resource optimizations.