Search Results for author:
Found 5 papers, 5 papers with code
GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction
Graph Anomaly Detection (GAD) is a technique used to identify abnormal nodes within graphs, finding applications in network security, fraud detection, social media spam detection, and various other domains.
Node Embedding using Mutual Information and Self-Supervision based Bi-level Aggregation
Graph Neural Networks (GNNs) learn low dimensional representations of nodes by aggregating information from their neighborhood in graphs.
Structure-Aware Hierarchical Graph Pooling using Information Bottleneck
Graph pooling is an essential ingredient of Graph Neural Networks (GNNs) in graph classification and regression tasks.
Unified Spatio-Temporal Modeling for Traffic Forecasting using Graph Neural Network
However, most state-of-the-art approaches have designed spatial-only (e. g. Graph Neural Networks) and temporal-only (e. g. Recurrent Neural Networks) modules to separately extract spatial and temporal features.
SST-GNN: Simplified Spatio-temporal Traffic forecasting model using Graph Neural Network
Most of the recent works employed graph neural networks(GNN) with multiple layers to capture the spatial dependency.
