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Dynamic Graph Node Classification via Time Augmentation
Node classification on dynamic graphs is challenging for two reasons.
Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph
Graph neural networks (GNNs) are deep learning models designed specifically for graph data, and they typically rely on node features as the input to the first layer.
DyFormer: A Scalable Dynamic Graph Transformer with Provable Benefits on Generalization Ability
To achieve efficient and scalable training, we propose temporal-union graph structure and its associated subgraph-based node sampling strategy.
Embedding Compression with Hashing for Efficient Representation Learning in Graph
When applying such type of networks on graph without node feature, one can extract simple graph-based node features (e. g., number of degrees) or learn the input node representation (i. e., embeddings) when training the network.
Event2Graph: Event-driven Bipartite Graph for Multivariate Time-series Anomaly Detection
Modeling inter-dependencies between time-series is the key to achieve high performance in anomaly detection for multivariate time-series data.
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