Search

Time series forecasting lies at the core of important real-world applications in many fields of science and engineering. The abundance of large time series datasets that consist of complex patterns and long-term dependencies has led to the development of various neural network architectures. Graph neural network approaches, which jointly learn a graph structure based on the correlation of raw values of multivariate time series while forecasting, have recently seen great success. However, such solutions are often costly to train and difficult to scale. In this paper, we propose TimeGNN, a method that learns dynamic temporal graph representations that can capture the evolution of inter-series patterns along with the correlations of multiple series. TimeGNN achieves inference times 4 to 80 times faster than other state-of-the-art graph-based methods while achieving comparable forecasting performance., QC 20240409Part of ISBN 9783031534676

Graph-based deep learning models are powerful in modeling spatio-temporal graphs for traffic forecasting. In practice, accurate forecasting models rely on sufficient traffic data, which may not be accessible in real-world applications. To address this problem, transfer learning methods are designed to transfer knowledge from the source graph with abundant data to the target graph with limited data. However, existing methods adopt pre-defined graph structures for knowledge extraction and transfer, which may be noisy or biased and negatively impact the performance of knowledge transfer. To address the problem, we propose TransGTR, a transferable structure learning framework for traffic forecasting that jointly learns and transfers the graph structures and forecasting models across cities. TransGTR consists of a node feature network, a structure generator, and a forecasting model. We train the node feature network with knowledge distillation to extract city-agnostic node features, such that the structure generator, taking the node features as inputs, can be transferred across both cities. Furthermore, we train the structure generator via a temporal decoupled regularization, such that the spatial features learned with the generated graphs share similar distributions across cities and thus facilitate knowledge transfer for the forecasting model. We evaluate TransGTR on real-world traffic speed datasets, where under a fair comparison, TransGTR outperforms state-of-the-art baselines by up to 5.4%. © 2023 ACM.

Next Point-of-Interest (POI) recommendation is an essential part of the flourishing location-based applications, where the demands of users are not only conditioned by their recent check-in behaviors but also by the critical influence stemming from geographical dependencies among POIs. Existing methods leverage Graph Neural Networks with the aid of pre-defined POI graphs to capture such indispensable correlations for modeling user preferences, assuming that the appropriate geographical dependencies among POIs could be pre-determined. However, the pre-defined graph structures are always far from the optimal graph topology due to noise and adaptability issues, which may decrease the expressivity of learned POI representations as well as the credibility of modeling user preferences. In this paper, we propose a novel Adaptive Graph Representation-enhanced Attention Network (AGRAN) for next POI recommendation, which explores the utilization of graph structure learning to replace the pre-defined static graphs for learning more expressive representations of POIs. In particular, we develop an adaptive POI graph matrix and learn it via similarity learning with POI embeddings, automatically capturing the underlying geographical dependencies for representation learning. Afterward, we incorporate the learned representations of POIs and personalized spatial-temporal information with an extension to the self-attention mechanism for capturing dynamic user preferences. Extensive experiments conducted on two real-world datasets validate the superior performance of our proposed method over state-of-the-art baselines. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

Learning an optimal chain graph from data is an important hard computational problem. We present a new approach to solve this problem for various objective functions without making any assumption on the probability distribution at hand. Our approach is based on encoding the learning problem declaratively using the answer set programming (ASP) paradigm. Empirical results show that our approach provides at least as accurate solutions as the best solutions provided by the existing algorithms, and overall provides better accuracy than any single previous algorithm.