6 research outputs found
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
Timely accurate traffic forecast is crucial for urban traffic control and
guidance. Due to the high nonlinearity and complexity of traffic flow,
traditional methods cannot satisfy the requirements of mid-and-long term
prediction tasks and often neglect spatial and temporal dependencies. In this
paper, we propose a novel deep learning framework, Spatio-Temporal Graph
Convolutional Networks (STGCN), to tackle the time series prediction problem in
traffic domain. Instead of applying regular convolutional and recurrent units,
we formulate the problem on graphs and build the model with complete
convolutional structures, which enable much faster training speed with fewer
parameters. Experiments show that our model STGCN effectively captures
comprehensive spatio-temporal correlations through modeling multi-scale traffic
networks and consistently outperforms state-of-the-art baselines on various
real-world traffic datasets.Comment: Proceedings of the 27th International Joint Conference on Artificial
Intelligenc
Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning
Subgraph-based graph representation learning (SGRL) has been recently
proposed to deal with some fundamental challenges encountered by canonical
graph neural networks (GNNs), and has demonstrated advantages in many important
data science applications such as link, relation and motif prediction. However,
current SGRL approaches suffer from scalability issues since they require
extracting subgraphs for each training or test query. Recent solutions that
scale up canonical GNNs may not apply to SGRL. Here, we propose a novel
framework SUREL for scalable SGRL by co-designing the learning algorithm and
its system support. SUREL adopts walk-based decomposition of subgraphs and
reuses the walks to form subgraphs, which substantially reduces the redundancy
of subgraph extraction and supports parallel computation. Experiments over six
homogeneous, heterogeneous and higher-order graphs with millions of nodes and
edges demonstrate the effectiveness and scalability of SUREL. In particular,
compared to SGRL baselines, SUREL achieves 10 speed-up with comparable
or even better prediction performance; while compared to canonical GNNs, SUREL
achieves 50% prediction accuracy improvement.Comment: This is an extended version of the full paper to appear in PVLDB
15.11(VLDB 2022
On the Inherent Privacy Properties of Discrete Denoising Diffusion Models
Privacy concerns have led to a surge in the creation of synthetic datasets,
with diffusion models emerging as a promising avenue. Although prior studies
have performed empirical evaluations on these models, there has been a gap in
providing a mathematical characterization of their privacy-preserving
capabilities. To address this, we present the pioneering theoretical
exploration of the privacy preservation inherent in discrete diffusion models
(DDMs) for discrete dataset generation. Focusing on per-instance differential
privacy (pDP), our framework elucidates the potential privacy leakage for each
data point in a given training dataset, offering insights into data
preprocessing to reduce privacy risks of the synthetic dataset generation via
DDMs. Our bounds also show that training with -sized data points leads to a
surge in privacy leakage from -pDP to -pDP during the transition from the pure
noise to the synthetic clean data phase, and a faster decay in diffusion
coefficients amplifies the privacy guarantee. Finally, we empirically verify
our theoretical findings on both synthetic and real-world datasets
Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting
Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of mid-and-long term prediction tasks and often neglect spatial and temporal dependencies. In this paper, we propose a novel deep learning framework, Spatio-Temporal Graph Convolutional Networks (STGCN), to tackle the time series prediction problem in traffic domain. Instead of applying regular convolutional and recurrent units, we formulate the problem on graphs and build the model with complete convolutional structures, which enable much faster training speed with fewer parameters. Experiments show that our model STGCN effectively captures comprehensive spatio-temporal correlations through modeling multi-scale traffic networks and consistently outperforms state-of-the-art baselines on various real-world traffic datasets