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Xiaoling LU

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5 papers

2 author rows

ECAI Conference 2025 Conference Paper

A Modality-Tailored Graph Modeling Framework for Urban Region Representation via Contrastive Learning

Yaya Zhao
Kaiqi Zhao 0001
Zixuan Tang
Zhiyuan Liu
Xiaoling Lu
Yalei Du

Graph-based models have emerged as a powerful paradigm for modeling multimodal urban data and learning region representations for various downstream tasks. However, existing approaches face two major limitations. (1) They typically employ identical graph neural network architectures across all modalities, failing to capture modality-specific structures and characteristics. (2) During the fusion stage, they often neglect spatial heterogeneity by assuming that the aggregation weights of different modalities remain invariant across regions, resulting in suboptimal representations. To address these issues, we propose MTGRR, a modality-tailored graph modeling framework for urban region representation, built upon a multimodal dataset comprising point of interest (POI), taxi mobility, land use, road element, remote sensing, and street view images. (1) MTGRR categorizes modalities into two groups based on spatial density and data characteristics: aggregated-level and point-level modalities. For aggregated-level modalities, MTGRR employs a mixture-of-experts (MoE) graph architecture, where each modality is processed by a dedicated expert GNN to capture distinct modality-specific characteristics. For the point-level modality, a dual-level GNN is constructed to extract fine-grained visual semantic features. (2) To obtain effective region representations under spatial heterogeneity, a spatially-aware multimodal fusion mechanism is designed to dynamically infer region-specific modality fusion weights. Building on this graph modeling framework, MTGRR further employs a joint contrastive learning strategy that integrates region aggregated-level, point-level, and fusion-level objectives to optimize region representations. Experiments on two real-world datasets across six modalities and three tasks demonstrate that MTGRR consistently outperforms state-of-the-art baselines, validating its effectiveness.

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JMLR Journal 2025 Journal Article

Fine-Grained Change Point Detection for Topic Modeling with Pitman-Yor Process

Feifei Wang
Zimeng Zhao
Ruimin Ye
Xiaoge Gu
Xiaoling LU

Identifying change points in dynamic text data is crucial for understanding the evolving nature of topics across various sources, such as news articles, scientific papers, and social media posts. While topic modeling has become a widely used technique for this purpose, capturing fine-grained shifts in individual topics over time remains a significant challenge. Traditional approaches typically use a two-stage process, separating topic modeling and change point detection. However, this separation can lead to information loss and inconsistency in capturing subtle changes in topic evolution. To address this issue, we propose TOPIC-PYP, a change point detection model specifically designed for fine-grained topic-level analysis, i.e., detecting change points for each individual topic. By leveraging the Pitman-Yor process, TOPIC-PYP effectively captures the dynamic evolution of topic meanings over time. Unlike traditional methods, TOPIC-PYP integrates topic modeling and change point detection into a unified framework, facilitating a more comprehensive understanding of the relationship between topic evolution and change points. Experimental evaluations on both synthetic and real-world datasets demonstrate the effectiveness of TOPIC-PYP in accurately detecting change points and generating high-quality topics. [abs] [ pdf ][ bib ] &copy JMLR 2025. ( edit, beta )

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NeurIPS Conference 2025 Conference Paper

MIHC: Multi-View Interpretable Hypergraph Neural Networks with Information Bottleneck for Chip Congestion Prediction

Zeyue Zhang
Heng Ping
Peiyu Zhang
Nikos Kanakaris
Xiaoling LU
Paul Bogdan
Xiongye Xiao

With AI advancement and increasing circuit complexity, efficient chip design through Electronic Design Automation (EDA) is critical. Fast and accurate congestion prediction in chip layout and routing can significantly enhance automated design performance. Existing congestion modeling methods are limited by (i) ineffective processing and fusion of multi-view circuit data information, and (ii) insufficient reliability and interpretability in the prediction process. To address these challenges, We propose M ulti-view I nterpretable H ypergraph for C hip ( MIHC ), a trustworthy 'multi-view hypergraph neural network'-based framework that (i) processes both graph and image information in unified hypergraph representations, capturing topological and geometric circuit data, and (ii) implements a novel subgraph Information Bottleneck mechanism identifying critical congestion-correlated regions to guide predictions. This represents the first attempt to incorporate such interpretability into congestion prediction through informative graph reasoning. Experiments show our model reduces NMAE by 16. 67% and 8. 57% in cell-based and grid-based predictions on ISPD2015, and 5. 26% and 2. 44% on CircuitNet-N28, respectively, compared to state-of-the-art methods. Rigorous cross-design generalization experiments further validate our method’s capability to handle entirely unseen circuit designs.

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IJCAI Conference 2024 Conference Paper

A Graph-based Representation Framework for Trajectory Recovery via Spatiotemporal Interval-Informed Seq2Seq

Yaya Zhao
Kaiqi Zhao
Zhiqian Chen
Yuanyuan Zhang
Yalei Du
Xiaoling LU

The prevalent issue in urban trajectory data usage, notably in low-sample rate datasets, revolves around the accuracy of travel time estimations, traffic flow predictions, and trajectory similarity measurements. Conventional methods, often relying on simplistic mixes of static road networks and raw GPS data, fail to adequately integrate both network and trajectory dimensions. Addressing this, the innovative GRFTrajRec framework offers a graph-based solution for trajectory recovery. Its key feature is a trajectory-aware graph representation, enhancing the understanding of trajectory-road network interactions and facilitating the extraction of detailed embedding features for road segments. Additionally, GRFTrajRec's trajectory representation acutely captures spatiotemporal attributes of trajectory points. Central to this framework is a novel spatiotemporal interval-informed seq2seq model, integrating an attention-enhanced transformer and a feature differences-aware decoder. This model specifically excels in handling spatiotemporal intervals, crucial for restoring missing GPS points in low-sample datasets. Validated through extensive experiments on two large real-life trajectory datasets, GRFTrajRec has proven its efficacy in significantly boosting prediction accuracy and spatial consistency.

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NeurIPS Conference 2024 Conference Paper

Conjugate Bayesian Two-step Change Point Detection for Hawkes Process

Zeyue Zhang
Xiaoling LU
Feng Zhou

The Bayesian two-step change point detection method is popular for the Hawkes process due to its simplicity and intuitiveness. However, the non-conjugacy between the point process likelihood and the prior requires most existing Bayesian two-step change point detection methods to rely on non-conjugate inference methods. These methods lack analytical expressions, leading to low computational efficiency and impeding timely change point detection. To address this issue, this work employs data augmentation to propose a conjugate Bayesian two-step change point detection method for the Hawkes process, which proves to be more accurate and efficient. Extensive experiments on both synthetic and real data demonstrate the superior effectiveness and efficiency of our method compared to baseline methods. Additionally, we conduct ablation studies to explore the robustness of our method concerning various hyperparameters.

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