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Found 6 papers, 5 papers with code
Balancing Summarization and Change Detection in Graph Streams
The parameter specifying the summary graph is then optimized so that the accuracy of change detection is guaranteed to suppress Type I error probability (probability of raising false alarms) to be less than a given confidence level.
Revisiting Mobility Modeling with Graph: A Graph Transformer Model for Next Point-of-Interest Recommendation
Extracting distinct fine-grained features unique to each piece of information is difficult since temporal information often includes spatial information, as users tend to visit nearby POIs.
MegaCRN: Meta-Graph Convolutional Recurrent Network for Spatio-Temporal Modeling
Spatio-temporal modeling as a canonical task of multivariate time series forecasting has been a significant research topic in AI community.
Spatio-Temporal Meta-Graph Learning for Traffic Forecasting
Traffic forecasting as a canonical task of multivariate time series forecasting has been a significant research topic in AI community.
Ranked #1 on
Traffic Prediction
on EXPY-TKY
Detecting Hierarchical Changes in Latent Variable Models
This paper addresses the issue of detecting hierarchical changes in latent variable models (HCDL) from data streams.
Online Robust and Adaptive Learning from Data Streams
We address the relation between the two parameters: one is the step size of the stochastic approximation, and the other is the threshold parameter of the norm of the stochastic update.
