GeoGNN is a two-tower GNN that learns geographic cell embeddings from adjacency graphs and matches them to temporal representations via dot-product similarity plus classification, improving geolocalization accuracy by ~27% on electricity datasets.
Webb, Irwin King, and Shirui Pan
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AlignGAD is a zero-shot generalized graph anomaly detection framework using a Global Unification Module, Clustering Module, and Node Discrepancy Scoring Module.
GNNs with sparsified mobility graphs outperform LSTMs for daily COVID-19 case forecasting in Brazil and China while LSTMs suffice for cumulative trends.
citing papers explorer
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GeoGNN: Time Series Geo-Localization using Two-Tower Graph Neural Networks
GeoGNN is a two-tower GNN that learns geographic cell embeddings from adjacency graphs and matches them to temporal representations via dot-product similarity plus classification, improving geolocalization accuracy by ~27% on electricity datasets.
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A Zero-shot Generalized Graph Anomaly Detection Framework via Node Reconstruction
AlignGAD is a zero-shot generalized graph anomaly detection framework using a Global Unification Module, Clustering Module, and Node Discrepancy Scoring Module.
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Leveraging graph neural networks and mobility data for COVID-19 forecasting
GNNs with sparsified mobility graphs outperform LSTMs for daily COVID-19 case forecasting in Brazil and China while LSTMs suffice for cumulative trends.