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Found 28 papers, 8 papers with code
Small Graph Is All You Need: DeepStateGNN for Scalable Traffic Forecasting
Unlike typical GNN methods that treat each traffic sensor as an individual graph node, DeepStateGNN clusters sensors into higher-level graph nodes, dubbed Deep State Nodes, based on various similarity criteria, resulting in a fixed number of nodes in a Deep State graph.
WaveGNN: Modeling Irregular Multivariate Time Series for Accurate Predictions
Accurately modeling and analyzing time series data is crucial for downstream applications across various fields, including healthcare, finance, astronomy, and epidemiology.
Forecasting Unseen Points of Interest Visits Using Context and Proximity Priors
To address this challenge, we propose a model designed to predict a new POI outside the training data as long as its context is aligned with the user's interests.
Towards Establishing Guaranteed Error for Learned Database Operations
Machine learning models have demonstrated substantial performance enhancements over non-learned alternatives in various fundamental data management operations, including indexing (locating items in an array), cardinality estimation (estimating the number of matching records in a database), and range-sum estimation (estimating aggregate attribute values for query-matched records).
TrajGPT: Controlled Synthetic Trajectory Generation Using a Multitask Transformer-Based Spatiotemporal Model
TrajGPT integrates the spatial and temporal models in a transformer architecture through a Bayesian probability model that ensures that the gaps in a visit sequence are filled in a spatiotemporally consistent manner.
Poly2Vec: Polymorphic Fourier-Based Encoding of Geospatial Objects for GeoAI Applications
Encoding geospatial objects is fundamental for geospatial artificial intelligence (GeoAI) applications, which leverage machine learning (ML) models to analyze spatial information.
Geo-Llama: Leveraging LLMs for Human Mobility Trajectory Generation with Spatiotemporal Constraints
Simulating human mobility data is essential for various application domains, including transportation, urban planning, and epidemic control, since real data are often inaccessible to researchers due to expensive costs and privacy issues.
Differentially Private Publication of Electricity Time Series Data in Smart Grids
We introduce {\em STPT (Spatio-Temporal Private Timeseries)}, a novel method for DP-compliant publication of electricity consumption data that analyzes spatio-temporal attributes and captures both micro and macro patterns by leveraging RNNs.
Dynamic GNNs for Precise Seizure Detection and Classification from EEG Data
This paper introduces NeuroGNN, a dynamic Graph Neural Network (GNN) framework that captures the dynamic interplay between the EEG electrode locations and the semantics of their corresponding brain regions.
BiasBuster: a Neural Approach for Accurate Estimation of Population Statistics using Biased Location Data
We show that statistical debiasing, although in some cases useful, often fails to improve accuracy.
Holistic Survey of Privacy and Fairness in Machine Learning
Privacy and fairness are two crucial pillars of responsible Artificial Intelligence (AI) and trustworthy Machine Learning (ML).
Learning Dynamic Graphs from All Contextual Information for Accurate Point-of-Interest Visit Forecasting
Forecasting the number of visits to Points-of-Interest (POI) in an urban area is critical for planning and decision-making for various application domains, from urban planning and transportation management to public health and social studies.
On Distribution Dependent Sub-Logarithmic Query Time of Learned Indexing
In this paper, we significantly strengthen this result, showing that under mild assumptions on data distribution, and the same space complexity as non-learned methods, learned indexes can answer queries in $O(\log\log n)$ expected query time.
Fair Spatial Indexing: A paradigm for Group Spatial Fairness
We propose techniques to mitigate location bias in machine learning.
Models and Mechanisms for Spatial Data Fairness
We introduce the concept of spatial data fairness to address the specific challenges of location data and spatial queries.
Gaussian Process for Trajectories
The Gaussian process is a powerful and flexible technique for interpolating spatiotemporal data, especially with its ability to capture complex trends and uncertainty from the input signal.
HAGEN: Homophily-Aware Graph Convolutional Recurrent Network for Crime Forecasting
Based on the homophily assumption of GNN, we propose a homophily-aware constraint to regularize the optimization of the region graph so that neighboring region nodes on the learned graph share similar crime patterns, thus fitting the mechanism of diffusion convolution.
SemiFed: Semi-supervised Federated Learning with Consistency and Pseudo-Labeling
Federated learning enables multiple clients, such as mobile phones and organizations, to collaboratively learn a shared model for prediction while protecting local data privacy.
Integer-arithmetic-only Certified Robustness for Quantized Neural Networks
Adversarial data examples have drawn significant attention from the machine learning and security communities.
Towards Accurate Spatiotemporal COVID-19 Risk Scores using High Resolution Real-World Mobility Data
As countries look towards re-opening of economic activities amidst the ongoing COVID-19 pandemic, ensuring public health has been challenging.
Kartta Labs: Collaborative Time Travel
We introduce the modular and scalable design of Kartta Labs, an open source, open data, and scalable system for virtually reconstructing cities from historical maps and photos.
Spatial Privacy Pricing: The Interplay between Privacy, Utility and Price in Geo-Marketplaces
Users concerned about privacy may want to charge more for data that pinpoints their location accurately, but may charge less for data that is more vague.
DETECT: Deep Trajectory Clustering for Mobility-Behavior Analysis
Identifying mobility behaviors in rich trajectory data is of great economic and social interest to various applications including urban planning, marketing and intelligence.
Cascade-BGNN: Toward Efficient Self-supervised Representation Learning on Large-scale Bipartite Graphs
Existing techniques either cannot be scaled to large-scale bipartite graphs that have limited labels or cannot exploit the unique structure of bipartite graphs, which have distinct node features in two domains.
m-TSNE: A Framework for Visualizing High-Dimensional Multivariate Time Series
Multivariate time series (MTS) have become increasingly common in healthcare domains where human vital signs and laboratory results are collected for predictive diagnosis.
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain.
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On Identifying Disaster-Related Tweets: Matching-based or Learning-based?
Social media such as tweets are emerging as platforms contributing to situational awareness during disasters.
Label Propagation on K-partite Graphs with Heterophily
In this paper, for the first time, we study label propagation in heterogeneous graphs under heterophily assumption.
