Author name cluster

Jingyuan Wang

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

27 papers

2 author rows

AAAI Conference 2026 Conference Paper

Hierarchical Frequency-Decomposition Graph Neural Networks for Road Network Representation Learning

Jingtian Ma
Jingyuan Wang
Leong Hou U

Road networks are critical infrastructures underpinning intelligent transportation systems and their related applications. Effective representation learning of road networks remains challenging due to the complex interplay between spatial structures and frequency characteristics in traffic patterns. Existing graph neural networks for modeling road networks predominantly fall into two paradigms: spatial-based methods that capture local topology but tend to over-smooth representations, and spectral-based methods that analyze global frequency components but often overlook localized variations. This spatial-spectral misalignment limits their modeling capacity for road networks exhibiting both coarse global trends and fine-grained local fluctuations. To bridge this gap, we propose HiFiNet, a novel hierarchical frequency-decomposition graph neural network that unifies spatial and spectral modeling. HiFiNet constructs a multi-level hierarchy of virtual nodes to enable localized frequency analysis, and employs a decomposition–updating–reconstruction framework with a topology-aware graph transformer to separately model and fuse low- and high-frequency signals. Theoretically justified and empirically validated on multiple real-world datasets across four downstream tasks, HiFiNet demonstrates superior performance and generalization ability in capturing effective road network representations.

PDF Details DOI

AAAI Conference 2026 Conference Paper

ICAD-LLM: One-for-All Anomaly Detection via In-Context Learning with Large Language Models

Zhongyuan Wu
Jingyuan Wang
Zexuan Cheng
Yilong Zhou
Weizhi Wang
Juhua Pu
Chao Li
Changqing Ma

Anomaly detection (AD) is a fundamental task of critical importance across numerous domains. Current systems increasingly operate in rapidly evolving environments that generate diverse yet interconnected data modalities—such as time series, system logs, and tabular records—as exemplified by modern IT systems. Effective AD methods in such environments must therefore possess two critical capabilities: (1) the ability to handle heterogeneous data formats within a unified framework, allowing the model to process and detect multiple modalities in a consistent manner during anomalous events; (2) a strong generalization ability to quickly adapt to new scenarios without extensive retraining. However, most existing methods fall short of these requirements, as they typically focus on single modalities and lack the flexibility to generalize across domains. To address this gap, we introduce a novel paradigm: In-Context Anomaly Detection (ICAD), where anomalies are defined by their dissimilarity to a relevant reference set of normal samples. Under this paradigm, we propose ICAD-LLM, a unified AD framework leveraging Large Language Models' in-context learning abilities to process heterogeneous data within a single model. Extensive experiments demonstrate that ICAD-LLM achieves competitive performance with task-specific AD methods and exhibits strong generalization to previously unseen tasks, which substantially reduces deployment costs and enables rapid adaptation to new environments. To the best of our knowledge, ICAD-LLM is the first model capable of handling anomaly detection tasks across diverse domains and modalities.

PDF Details DOI

AAAI Conference 2026 Conference Paper

Multi-Agent Pointer Transformer: Seq-to-Seq Reinforcement Learning for Multi-Vehicle Dynamic Pickup-Delivery Problems

Zengyu Zou
Jingyuan Wang
Yixuan Huang
Junjie Wu

This paper addresses the cooperative Multi-Vehicle Dynamic Pickup and Delivery Problem with Stochastic Requests (MVDPDPSR) and proposes an end-to-end centralized decision-making framework based on sequence-to-sequence, named Multi-Agent Pointer Transformer (MAPT). MVDPDPSR is an extension of the vehicle routing problem and a spatio-temporal system optimization problem, widely applied in scenarios such as on-demand delivery. Classical operations research methods face bottlenecks in computational complexity and time efficiency when handling large-scale dynamic problems. Although existing reinforcement learning methods have achieved some progress, they still encounter several challenges: 1) Independent decoding across multiple vehicles fails to model joint action distributions; 2) The feature extraction network struggles to capture inter-entity relationships; 3) The joint action space is exponentially large. To address these issues, we designed the MAPT framework, which employs a Transformer Encoder to extract entity representations, combines a Transformer Decoder with a Pointer Network to generate joint action sequences in an AutoRegressive manner, and introduces a Relation-Aware Attention module to capture inter-entity relationships. Additionally, we guide the model's decision-making using informative priors to facilitate effective exploration. Experiments on 8 datasets demonstrate that MAPT significantly outperforms existing baseline methods in terms of performance and exhibits substantial computational time advantages compared to classical operations research methods.

PDF Details DOI

AAAI Conference 2026 Conference Paper

Re-architecting Personalized Federated Learning for Demanding Edge Environments

Quyang Pan
Sheng Sun
Tingting Wi
Zhiyuan Wu
Yuwei Wang
Min Liu
Bo Gao
Jingyuan Wang

Federated Edge Learning (FEL) has emerged as a promising approach for enabling edge devices to collaboratively train machine learning models while preserving data privacy. Despite its advantages, practical FEL deployment faces significant challenges related to device constraints and device-server interactions, necessitating heterogeneous, user-adaptive model training with limited and uncertain communication. While knowledge cache-driven federated learning offers a promising FEL solution for demanding edge environments, its logits-based interaction design provides poor richness of exchanged information for on-device model optimization. To tackle this issue, we introduce DistilCacheFL, a novel personalized FEL architecture that enhances the exchange of optimization insights while delivering state-of-the-art performance with efficient communication. DistilCacheFL incorporates the benefits of both dataset distillation and knowledge cache-driven federated learning by storing and organizing distilled data as knowledge in the server-side knowledge cache, allowing devices to periodically download and utilize personalized knowledge for local model optimization. Moreover, a device-centric cache sampling strategy is introduced to tailor transferred knowledge for individual devices within controlled communication bandwidth. Extensive experiments on five datasets covering image recognition, audio understanding, and mobile sensor data mining tasks demonstrate that (1) DistilCacheFL significantly outperforms state-of-the-art methods regardless of model structures, data distributions, and modalities. (2) DistilCacheFL can train splendid personalized on-device models with at least 28.6 improvement in communication efficiency.

PDF Details DOI

TIST Journal 2026 Journal Article

Reconciling Cognitive Modeling with Knowledge Forgetting: A Continuous Time-aware General Neural Network Framework

Ziwen Wang
Jingyuan Wang
Haiping Ma
Hengshu Zhu
Shangshang Yang
Xiaoshan Yu
Shuhuan Liu
Haifeng Zhang

Cognitive modeling, as an emerging technology in the field of computer-aided education, aims to explore students’ knowledge levels and learning abilities to achieve various intelligent educational applications. Although some existing work focuses on addressing the problem of student forgetting, it is still a less explored area how to naturally integrate the forgetting effect caused by the time interval between answering exercises into student knowledge state modeling. Additionally, traditional cognitive modeling methods mostly assume that students answer exercises one by one, which often does not align with real answering behavior and cannot be directly extended to diverse learning scenarios. Therefore, in this article, we propose a Continuous Time-based Neural Cognitive (CT-NC) framework and several implemented models (CT-NCM and two extensions) to effectively integrate the dynamic and continuous characteristics of knowledge forgetting into student learning process modeling, making it more natural. Specifically, we adopt a specially designed learning event encoding method to adjust the neural Hawkes process to capture the relationship between knowledge learning and forgetting over continuous time. Furthermore, we propose a customizable learning function to jointly model the changes in different knowledge states and their interaction with each practice moment. In the end, we demonstrate an extension CT-NCM+ that can adapt well to diverse learning scenarios, indicating that CT-NCM can solve real-world problems by flexibly adjusting its structure. Extensive experimental results on real datasets clearly demonstrate that CT-NCM and CT-NCM+ outperform the current state-of-the-art KT methods in student performance prediction, while our work points out a realistic research direction for KT and demonstrates its interpretability in knowledge learning visualization.

Details DOI

AAAI Conference 2026 Conference Paper

RoSA: Enhancing Parameter-Efficient Fine-Tuning via RoPE-aware Selective Adaptation in Large Language Models

Dayan Pan
Jingyuan Wang
Yilong Zhou
Jiawei Cheng
Pengyue Jia
Xiangyu Zhao

Fine-tuning large language models is essential for task-specific adaptation, yet it remains computationally prohibitive. Parameter-Efficient Fine-Tuning (PEFT) methods have emerged as a solution, but current approaches typically ignore the distinct roles of model components and the heterogeneous importance across layers, thereby limiting adaptation efficiency. Motivated by the observation that Rotary Position Embeddings (RoPE) induce critical activations in the low-frequency dimensions of attention states, we propose RoPE-aware Selective Adaptation (RoSA), a novel PEFT framework that allocates trainable parameters in a more targeted and effective manner. RoSA comprises a RoPE-aware Attention Enhancement (RoAE) module, which selectively enhances the low-frequency components of RoPE-influenced attention states, and a Dynamic Layer Selection (DLS) strategy that adaptively identifies and updates the most critical layers based on LayerNorm gradient norms. By combining dimension-wise enhancement with layer-wise adaptation, RoSA achieves more targeted and efficient fine-tuning. Extensive experiments on fifteen commonsense and arithmetic benchmarks demonstrate that RoSA outperforms mainstream PEFT methods under comparable trainable parameters.

PDF Details DOI

AAAI Conference 2026 Conference Paper

Task-Aware Meta-Learning on Heterogeneous Knowledge Graph for POI Recommendation

Jingyuan Wang
Zhichun Wang
Tong Lu
Yiming Guan

Point-of-Interest (POI) recommendation plays a pivotal role in location-based services by guiding users to discover new and relevant places. While graph-based methods have shown promising results, effectively modeling the diversity and dynamics of user preferences remains a key challenge. Addressing this requires richer representations of both POIs and user interests, as well as more adaptive learning strategies. In this work, we propose TMHKG, a Task-aware Meta-learning framework with a Heterogeneous Knowledge Graph for POI recommendation. To enhance representation learning, TMHKG constructs a dual-view POI knowledge graph that integrates geographical proximity and user-aware category transitions, and models users' evolving interests from sequential visit histories. On top of enriched features, TMHKG adopts a task-aware meta-learning paradigm, treating each user's recommendation task as a separate meta-task. A generalizable recommendation policy is first learned from diverse training tasks and then quickly adapted to each user's unique behavior, enabling highly personalized predictions. Extensive experiments on two real-world datasets demonstrate that TMHKG consistently outperforms state-of-the-art baselines, highlighting its effectiveness in capturing complex user-POI interactions.

PDF Details DOI

ICML Conference 2025 Conference Paper

Approximation to Smooth Functions by Low-Rank Swish Networks

Zimeng Li
Hongjun Li
Jingyuan Wang
Ke Tang

While deep learning has witnessed remarkable achievements in a wide range of applications, its substantial computational cost imposes limitations on application scenarios of neural networks. To alleviate this problem, low-rank compression is proposed as a class of efficient and hardware-friendly network compression methods, which reduce computation by replacing large matrices in neural networks with products of two small ones. In this paper, we implement low-rank networks by inserting a sufficiently narrow linear layer without bias between each of two adjacent nonlinear layers. We prove that low-rank Swish networks with a fixed depth are capable of approximating any function from the Hölder ball $\mathcal{C}^{\beta, R}([0, 1]^d)$ within an arbitrarily small error where $\beta$ is the smooth parameter and $R$ is the radius. Our proposed constructive approximation ensures that the width of linear hidden layers required for approximation is no more than one-third of the width of nonlinear layers, which implies that the computational cost can be decreased by at least one-third compared with a network with the same depth and width of nonlinear layers but without narrow linear hidden layers. Our theoretical finding can offer a theoretical basis for low-rank compression from the perspective of universal approximation theory.

Details

AAAI Conference 2025 Conference Paper

Bridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning

Chengkai Han
Jingyuan Wang
Yongyao Wang
Xie Yu
Hao Lin
Chao Li
Junjie Wu

Effective urban traffic management is vital for sustainable city development, relying on intelligent systems with machine learning tasks such as traffic flow prediction and travel time estimation. Traditional approaches usually focus on static road network and trajectory representation learning, and overlook the dynamic nature of traffic states and trajectories, which is crucial for downstream tasks. To address this gap, we propose TRACK, a novel framework to bridge traffic state and trajectory data for dynamic road network and trajectory representation learning. TRACK leverages graph attention networks (GAT) to encode static and spatial road segment features, and introduces a transformer-based model for trajectory representation learning. By incorporating transition probabilities from trajectory data into GAT attention weights, TRACK captures dynamic spatial features of road segments. Meanwhile, TRACK designs a traffic transformer encoder to capture the spatial-temporal dynamics of road segments from traffic state data. To further enhance dynamic representations, TRACK proposes a co-attentional transformer encoder and a trajectory-traffic state matching task. Extensive experiments on real-life urban traffic datasets demonstrate the superiority of TRACK over state-of-the-art baselines. Case studies confirm TRACK’s ability to capture spatial-temporal dynamics effectively.

PDF Details DOI

NeurIPS Conference 2025 Conference Paper

Cross City Traffic Flow Generation via Retrieval Augmented Diffusion Model

Yudong Li
Jingyuan Wang
Xie Yu
Peiyu Wang
Qian Huang

Traffic flow data are of great value in smart city applications. However, limited by data collection costs and privacy sensitivity, it is rather difficult to obtain large-scale traffic flow data. Therefore, various data generation methods have been proposed in the literature. Nevertheless, these methods often require data from a specific city for training and are difficult to directly apply to new cities lacking data. To address this problem, this paper proposes a retrieval-augmented diffusion generation model with representation alignment. We use data from multiple source cities for training, extract consistent representations across multiple cities, and leverage retrieval-augmented generation (RAG) technology to incorporate historical data from source cities under similar conditions into the condition, aiming to improve the accuracy of data generation in the target city. Experiments on four real-world datasets demonstrate that, compared with existing deep learning methods, our method achieves better cross-city transfer performance.

PDF Details

ICML Conference 2025 Conference Paper

Distributionally Robust Policy Learning under Concept Drifts

Jingyuan Wang
Zhimei Ren
Ruohan Zhan
Zhengyuan Zhou

Distributionally robust policy learning aims to find a policy that performs well under the worst-case distributional shift, and yet most existing methods for robust policy learning consider the worst-case joint distribution of the covariate and the outcome. The joint-modeling strategy can be unnecessarily conservative when we have more information on the source of distributional shifts. This paper studies a more nuanced problem — robust policy learning under the concept drift, when only the conditional relationship between the outcome and the covariate changes. To this end, we first provide a doubly-robust estimator for evaluating the worst-case average reward of a given policy under a set of perturbed conditional distributions. We show that the policy value estimator enjoys asymptotic normality even if the nuisance parameters are estimated with a slower-than-root-$n$ rate. We then propose a learning algorithm that outputs the policy maximizing the estimated policy value within a given policy class $\Pi$, and show that the sub-optimality gap of the proposed algorithm is of the order $\kappa(\Pi)n^{-1/2}$, where $\kappa(\Pi)$ is the entropy integral of $\Pi$ under the Hamming distance and $n$ is the sample size. A matching lower bound is provided to show the optimality of the rate. The proposed methods are implemented and evaluated in numerical studies, demonstrating substantial improvement compared with existing benchmarks.

Details

AAAI Conference 2025 Conference Paper

GTG: Generalizable Trajectory Generation Model for Urban Mobility

Jingyuan Wang
Yujing Lin
Yudong Li

Trajectory data mining is crucial for smart city management. However, collecting large-scale trajectory datasets is challenging due to factors such as commercial conflicts and privacy regulations. Therefore, we urgently need trajectory generation techniques to address this issue. Existing trajectory generation methods rely on the global road network structure of cities. When the road network structure changes, these methods are often not transferable to other cities. In fact, there exist invariant mobility patterns between different cities: 1) People prefer paths with the minimal travel cost; 2) The travel cost of roads has an invariant relationship with the topological features of the road network. Based on the above insight, this paper proposes a Generalizable Trajectory Generation model (GTG). The model consists of three parts: 1) Extracting city-invariant road representation based on Space Syntax method; 2) Cross-city travel cost prediction through disentangled adversarial training; 3) Travel preference learning by shortest path search and preference update. By learning invariant movement patterns, the model is capable of generating trajectories in new cities. Experiments on three datasets demonstrates that our model significantly outperforms existing models in terms of generalization ability.

PDF Details DOI

IJCAI Conference 2025 Conference Paper

HygMap: Representing All Types of Map Entities via Heterogeneous Hypergraph

Yifan Yang
Jingyuan Wang
Xie Yu
Yibang Tang

Maps are crucial for various smart city applications as a core component of city geographic information systems (GIS). Developing effective Map Entity Representation Learning methods can extract semantic information for downstream tasks like crime rate prediction and land use classification, with significant application potential. A map comprises three entity types: land parcels, road segments, and points of interest. Most existing methods focus on a single entity type, losing inter-entity relationships and weakening representation effectiveness for real-world applications. Thus, jointly modelling and representing multiple map entity types is essential. However, designing a unified framework is challenging due to map data's unstructured, complex, and heterogeneous nature. We propose a novel method, HygMap, to represent all map entity types. We model the map as a heterogeneous hypergraph, design an encoder for map entities, and introduce a hybrid self-supervised training scheme. This architecture comprehensively captures the heterogeneous relationships among map entities at different levels. Experiments on nine downstream tasks with two real-world datasets show that our framework outperforms all baselines, with good computational efficiency and scalability.

PDF Details DOI

AAAI Conference 2025 Conference Paper

POI-Enhancer: An LLM-based Semantic Enhancement Framework for POI Representation Learning

Jiawei Cheng
Jingyuan Wang
Yichuan Zhang
Jiahao Ji
Yuanshao Zhu
Zhibo Zhang
Xiangyu Zhao

POI representation learning plays a crucial role in handling tasks related to user mobility data. Recent studies have shown that enriching POI representations with multimodal information can significantly enhance their task performance. Previously, the textual information incorporated into POI representations typically involved only POI categories or check-in content, leading to relatively weak textual features in existing methods. In contrast, large language models (LLMs) trained on extensive text data have been found to possess rich textual knowledge. However leveraging such knowledge to enhance POI representation learning presents two key challenges: first, how to extract POI-related knowledge from LLMs effectively, and second, how to integrate the extracted information to enhance POI representations. To address these challenges, we propose POI-Enhancer, a portable framework that leverages LLMs to improve POI representations produced by classic POI learning models. We first design three specialized prompts to extract semantic information from LLMs efficiently. Then, the Dual Feature Alignment module enhances the quality of the extracted information, while the Semantic Feature Fusion module preserves its integrity. The Cross Attention Fusion module then fully adaptively integrates such high-quality information into POI representations and Multi-View Contrastive Learning further injects human-understandable semantic information into these representations. Extensive experiments on three real-world datasets demonstrate the effectiveness of our framework, showing significant improvements across all baseline representations.

PDF Details DOI

ICML Conference 2025 Conference Paper

ProDiff: Prototype-Guided Diffusion for Minimal Information Trajectory Imputation

Tianci Bu
Le Zhou
Wenchuan Yang
Jianhong Mou
Kang Yang
Suoyi Tan
Feng Yao
Jingyuan Wang

Trajectory data is crucial for various applications but often suffers from incompleteness due to device limitations and diverse collection scenarios. Existing imputation methods rely on sparse trajectory or travel information, such as velocity, to infer missing points. However, these approaches assume that sparse trajectories retain essential behavioral patterns, which place significant demands on data acquisition and overlook the potential of large-scale human trajectory embeddings. To address this, we propose ProDiff, a trajectory imputation framework that uses only two endpoints as minimal information. It integrates prototype learning to embed human movement patterns and a denoising diffusion probabilistic model for robust spatiotemporal reconstruction. Joint training with a tailored loss function ensures effective imputation. ProDiff outperforms state-of-the-art methods, improving accuracy by 6. 28% on FourSquare and 2. 52% on WuXi. Further analysis shows a 0. 927 correlation between generated and real trajectories, demonstrating the effectiveness of our approach.

Details

ICML Conference 2024 Conference Paper

Adaptively Learning to Select-Rank in Online Platforms

Jingyuan Wang
Perry Dong
Ying Jin
Ruohan Zhan
Zhengyuan Zhou

Ranking algorithms are fundamental to various online platforms across e-commerce sites to content streaming services. Our research addresses the challenge of adaptively ranking items from a candidate pool for heterogeneous users, a key component in personalizing user experience. We develop a user response model that considers diverse user preferences and the varying effects of item positions, aiming to optimize overall user satisfaction with the ranked list. We frame this problem within a contextual bandits framework, with each ranked list as an action. Our approach incorporates an upper confidence bound to adjust predicted user satisfaction scores and selects the ranking action that maximizes these adjusted scores, efficiently solved via maximum weight imperfect matching. We demonstrate that our algorithm achieves a cumulative regret bound of $O(d\sqrt{NKT})$ for ranking $K$ out of $N$ items in a $d$-dimensional context space over $T$ rounds, under the assumption that user responses follow a generalized linear model. This regret alleviates dependence on the ambient action space, whose cardinality grows exponentially with $N$ and $K$ (thus rendering direct application of existing adaptive learning algorithms – such as UCB or Thompson sampling – infeasible). Experiments conducted on both simulated and real-world datasets demonstrate our algorithm outperforms the baseline.

Details

AAAI Conference 2024 Conference Paper

Full Bayesian Significance Testing for Neural Networks

Zehua Liu
Zimeng Li
Jingyuan Wang
Yue He

Significance testing aims to determine whether a proposition about the population distribution is the truth or not given observations. However, traditional significance testing often needs to derive the distribution of the testing statistic, failing to deal with complex nonlinear relationships. In this paper, we propose to conduct Full Bayesian Significance Testing for neural networks, called nFBST, to overcome the limitation in relationship characterization of traditional approaches. A Bayesian neural network is utilized to fit the nonlinear and multi-dimensional relationships with small errors and avoid hard theoretical derivation by computing the evidence value. Besides, nFBST can test not only global significance but also local and instance-wise significance, which previous testing methods don't focus on. Moreover, nFBST is a general framework that can be extended based on the measures selected, such as Grad-nFBST, LRP-nFBST, DeepLIFT-nFBST, LIME-nFBST. A range of experiments on both simulated and real data are conducted to show the advantages of our method.

PDF Details DOI

IJCAI Conference 2024 Conference Paper

Full Bayesian Significance Testing for Neural Networks in Traffic Forecasting

Zehua Liu
Jingyuan Wang
Zimeng Li
Yue He

Due to the complex and dynamic traffic contexts, the interpretability and uncertainty of traffic forecasting have gained increasing attention. Significance testing is a powerful tool in statistics used to determine whether a hypothesis is valid, facilitating the identification of pivotal features that predominantly contribute to the true relationship. However, existing works mainly regard traffic forecasting as a deterministic problem, making it challenging to perform effective significance testing. To fill this gap, we propose to conduct Full Bayesian Significance Testing for Neural Networks in Traffic Forecasting, namely ST-nFBST. A Bayesian neural network is utilized to capture the complicated traffic relationships through an optimization function resolved in the context of aleatoric uncertainty and epistemic uncertainty. Thereupon, ST-nFBST can achieve the significance testing by means of a delicate grad-based evidence value, further capturing the inherent traffic schema for better spatiotemporal modeling. Extensive experiments are conducted on METR-LA and PEMS-BAY to verify the advantages of our method in terms of uncertainty analysis and significance testing, helping the interpretability and promotion of traffic forecasting.

PDF Details DOI

AAAI Conference 2023 Conference Paper

Continuous Trajectory Generation Based on Two-Stage GAN

Wenjun Jiang
Wayne Xin Zhao
Jingyuan Wang
Jiawei Jiang

Simulating the human mobility and generating large-scale trajectories are of great use in many real-world applications, such as urban planning, epidemic spreading analysis, and geographic privacy protect. Although many previous works have studied the problem of trajectory generation, the continuity of the generated trajectories has been neglected, which makes these methods useless for practical urban simulation scenarios. To solve this problem, we propose a novel two-stage generative adversarial framework to generate the continuous trajectory on the road network, namely TS-TrajGen, which efficiently integrates prior domain knowledge of human mobility with model-free learning paradigm. Specifically, we build the generator under the human mobility hypothesis of the A* algorithm to learn the human mobility behavior. For the discriminator, we combine the sequential reward with the mobility yaw reward to enhance the effectiveness of the generator. Finally, we propose a novel two-stage generation process to overcome the weak point of the existing stochastic generation process. Extensive experiments on two real-world datasets and two case studies demonstrate that our framework yields significant improvements over the state-of-the-art methods.

PDF Details DOI

AAAI Conference 2023 Conference Paper

PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction

Jiawei Jiang
Chengkai Han
Wayne Xin Zhao
Jingyuan Wang

As a core technology of Intelligent Transportation System, traffic flow prediction has a wide range of applications. The fundamental challenge in traffic flow prediction is to effectively model the complex spatial-temporal dependencies in traffic data. Spatial-temporal Graph Neural Network (GNN) models have emerged as one of the most promising methods to solve this problem. However, GNN-based models have three major limitations for traffic prediction: i) Most methods model spatial dependencies in a static manner, which limits the ability to learn dynamic urban traffic patterns; ii) Most methods only consider short-range spatial information and are unable to capture long-range spatial dependencies; iii) These methods ignore the fact that the propagation of traffic conditions between locations has a time delay in traffic systems. To this end, we propose a novel Propagation Delay-aware dynamic long-range transFormer, namely PDFormer, for accurate traffic flow prediction. Specifically, we design a spatial self-attention module to capture the dynamic spatial dependencies. Then, two graph masking matrices are introduced to highlight spatial dependencies from short- and long-range views. Moreover, a traffic delay-aware feature transformation module is proposed to empower PDFormer with the capability of explicitly modeling the time delay of spatial information propagation. Extensive experimental results on six real-world public traffic datasets show that our method can not only achieve state-of-the-art performance but also exhibit competitive computational efficiency. Moreover, we visualize the learned spatial-temporal attention map to make our model highly interpretable.

PDF Details DOI

AAAI Conference 2023 Conference Paper

Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction

Jiahao Ji
Jingyuan Wang
Chao Huang
Junjie Wu
Boren Xu
Zhenhe Wu
Junbo Zhang
Yu Zheng

Robust prediction of citywide traffic flows at different time periods plays a crucial role in intelligent transportation systems. While previous work has made great efforts to model spatio-temporal correlations, existing methods still suffer from two key limitations: i) Most models collectively predict all regions' flows without accounting for spatial heterogeneity, i.e., different regions may have skewed traffic flow distributions. ii) These models fail to capture the temporal heterogeneity induced by time-varying traffic patterns, as they typically model temporal correlations with a shared parameterized space for all time periods. To tackle these challenges, we propose a novel Spatio-Temporal Self-Supervised Learning (ST-SSL) traffic prediction framework which enhances the traffic pattern representations to be reflective of both spatial and temporal heterogeneity, with auxiliary self-supervised learning paradigms. Specifically, our ST-SSL is built over an integrated module with temporal and spatial convolutions for encoding the information across space and time. To achieve the adaptive spatio-temporal self-supervised learning, our ST-SSL first performs the adaptive augmentation over the traffic flow graph data at both attribute- and structure-levels. On top of the augmented traffic graph, two SSL auxiliary tasks are constructed to supplement the main traffic prediction task with spatial and temporal heterogeneity-aware augmentation. Experiments on four benchmark datasets demonstrate that ST-SSL consistently outperforms various state-of-the-art baselines. Since spatio-temporal heterogeneity widely exists in practical datasets, the proposed framework may also cast light on other spatial-temporal applications. Model implementation is available at https://github.com/Echo-Ji/ST-SSL.

PDF Details DOI

IJCAI Conference 2022 Conference Paper

Reconciling Cognitive Modeling with Knowledge Forgetting: A Continuous Time-aware Neural Network Approach

Haiping Ma
Jingyuan Wang
Hengshu Zhu
Xin Xia
Haifeng Zhang
Xingyi Zhang
Lei Zhang

As an emerging technology of computer-aided education, cognitive modeling aims at discovering the knowledge proficiency or learning ability of students, which can enable a wide range of intelligent educational applications. While considerable efforts have been made in this direction, a long-standing research challenge is how to naturally integrate the forgetting mechanism into the learning process of knowledge concepts. To this end, in this paper, we propose a novel Continuous Time based Neural Cognitive Modeling(CT-NCM) approach to integrate the dynamism and continuity of knowledge forgetting into students' learning process modeling in a realistic manner. To be specific, we first adapt the neural Hawkes process with a specially-designed learning event encoding method to model the relationship between knowledge learning and forgetting with continuous time. Then, we propose a learning function with extendable settings to jointly model the change of different knowledge states and their interactions with the exercises at each moment. In this way, CT-NCM can simultaneously predict the future knowledge state and exercise performance of students. Finally, we conduct extensive experiments on five real-world datasets with various benchmark methods. The experimental results clearly validate the effectiveness of CT-NCM and show its interpretability in terms of knowledge learning visualization.

PDF Details DOI

AAAI Conference 2022 Conference Paper

STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction

Jiahao Ji
Jingyuan Wang
Zhe Jiang
Jiawei Jiang
Hu Zhang

High-performance traffic flow prediction model designing, a core technology of Intelligent Transportation System, is a long-standing but still challenging task for industrial and academic communities. The lack of integration between physical principles and data-driven models is an important reason for limiting the development of this field. In the literature, physics-based methods can usually provide a clear interpretation of the dynamic process of traffic flow systems but are with limited accuracy, while data-driven methods, especially deep learning with black-box structures, can achieve improved performance but can not be fully trusted due to lack of a reasonable physical basis. To bridge the gap between purely data-driven and physics-driven approaches, we propose a physics-guided deep learning model named Spatio- Temporal Differential Equation Network (STDEN), which casts the physical mechanism of traffic flow dynamics into a deep neural network framework. Specifically, we assume the traffic flow on road networks is driven by a latent potential energy field (like water flows are driven by the gravity field), and model the spatio-temporal dynamic process of the potential energy field as a differential equation network. STDEN absorbs both the performance advantage of data-driven models and the interpretability of physics-based models, so is named a physics-guided prediction model. Experiments on three real-world traffic datasets in Beijing show that our model outperforms state-of-the-art baselines by a significant margin. A case study further verifies that STDEN can capture the mechanism of urban traffic and generate accurate predictions with physical meaning. The proposed framework of differential equation network modeling may also cast light on other similar applications.

PDF Details

AAAI Conference 2019 Conference Paper

SVM-Based Deep Stacking Networks

Jingyuan Wang
Kai Feng
Junjie Wu

The deep network model, with the majority built on neural networks, has been proved to be a powerful framework to represent complex data for high performance machine learning. In recent years, more and more studies turn to nonneural network approaches to build diverse deep structures, and the Deep Stacking Network (DSN) model is one of such approaches that uses stacked easy-to-learn blocks to build a parameter-training-parallelizable deep network. In this paper, we propose a novel SVM-based Deep Stacking Network (SVM-DSN), which uses the DSN architecture to organize linear SVM classifiers for deep learning. A BP-like layer tuning scheme is also proposed to ensure holistic and local optimizations of stacked SVMs simultaneously. Some good math properties of SVM, such as the convex optimization, is introduced into the DSN framework by our model. From a global view, SVM-DSN can iteratively extract data representations layer by layer as a deep neural network but with parallelizability, and from a local view, each stacked SVM can converge to its optimal solution and obtain the support vectors, which compared with neural networks could lead to interesting improvements in anti-saturation and interpretability. Experimental results on both image and text data sets demonstrate the excellent performances of SVM-DSN compared with some competitive benchmark models.

PDF Details

AAAI Conference 2018 Conference Paper

CD-CNN: A Partially Supervised Cross-Domain Deep Learning Model for Urban Resident Recognition

Jingyuan Wang
Xu He
Ze Wang
Junjie Wu
Nicholas Jing Yuan
Xing Xie
Zhang Xiong

Driven by the wave of urbanization in recent decades, the research topic about migrant behavior analysis draws great attention from both academia and the government. Nevertheless, subject to the cost of data collection and the lack of modeling methods, most of existing studies use only questionnaire surveys with sparse samples and non-individual level statistical data to achieve coarse-grained studies of migrant behaviors. In this paper, a partially supervised cross-domain deep learning model named CD-CNN is proposed for migrant/native recognition using mobile phone signaling data as behavioral features and questionnaire survey data as incomplete labels. Speciﬁcally, CD-CNN features in decomposing the mobile data into location domain and communication domain, and adopts a joint learning framework that combines two convolutional neural networks with a feature balancing scheme. Moreover, CD-CNN employs a three-step algorithm for training, in which the co-training step is of great value to partially supervised cross-domain learning. Comparative experiments on the city Wuxi demonstrate the high predictive power of CD-CNN. Two interesting applications further highlight the ability of CD-CNN for in-depth migrant behavioral analysis.

PDF Details

AAAI Conference 2017 Conference Paper

Coupling Implicit and Explicit Knowledge for Customer Volume Prediction

Jingyuan Wang
Yating Lin
Junjie Wu
Zhong Wang
Zhang Xiong

Customer volume prediction, which predicts the volume from a customer source to a service place, is a very important technique for location selection, market investigation, and other related applications. Most of traditional methods only make use of partial information for either supervised or unsupervised modeling, which cannot well integrate overall available knowledge. In this paper, we propose a method titled GR- NMF for jointly modeling both implicit correlations hidden inside customer volumes and explicit geographical knowledge via an integrated probabilistic framework. The effectiveness of GR-NMF in coupling all-round knowledge is veriﬁed over a real-life outpatient dataset under different scenarios. GR-NMF shows particularly evident advantages to all baselines in location selection with the cold-start challenge.

PDF Details

IS Journal 2012 Journal Article

Product Feature Grouping for Opinion Mining

Zhongwu Zhai
Bing Liu
Jingyuan Wang
Hua Xu
Peifa Jia

A constrained semisupervised learning method classifies words and phrases into feature groups, making it easier to produce an opinion summary of various product reviews.

Details DOI