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Found 4 papers, 3 papers with code
CEP3: Community Event Prediction with Neural Point Process on Graph
Many real world applications can be formulated as event forecasting on Continuous Time Dynamic Graphs (CTDGs) where the occurrence of a timed event between two entities is represented as an edge along with its occurrence timestamp in the graphs. However, most previous works approach the problem in compromised settings, either formulating it as a link prediction task on the graph given the event time or a time prediction problem given which event will happen next.
APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding
Limited by the time complexity of querying k-hop neighbors in a graph database, most graph algorithms cannot be deployed online and execute millisecond-level inference.
One-Class Graph Neural Networks for Anomaly Detection in Attributed Networks
Since traditional anomaly detection methods are stable, robust and easy to use, it is vitally important to generalize them to graph data.
adVAE: A self-adversarial variational autoencoder with Gaussian anomaly prior knowledge for anomaly detection
We assume that both the anomalous and the normal prior distribution are Gaussian and have overlaps in the latent space.
