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Xing Cai

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3 papers

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NeurIPS Conference 2025 Conference Paper

One for All: Universal Topological Primitive Transfer for Graph Structure Learning

Yide Qiu
Tong Zhang
Xing Cai
Hui Yan
Zhen Cui

The non-Euclidean geometry inherent in graph structures fundamentally impedes cross-graph knowledge transfer. Drawing inspiration from texture transfer in computer vision, we pioneer topological primitives as transferable semantic units for graph structural knowledge. To address three critical barriers - the absence of specialized benchmarks, aligned semantic representations, and systematic transfer methodologies - we present G²SN-Transfer, a unified framework comprising: (i) TopoGraph-Mapping that transforms non-Euclidean graphs into transferable sequences via topological primitive distribution dictionaries; (ii) G²SN, a dual-stream architecture learning text-topology aligned representations through contrastive alignment; and (iii) AdaCross-Transfer, a data-adaptive knowledge transfer mechanism leveraging cross-attention for both full-parameter and parameter-frozen scenarios. Particularly, G²SN is a dual-stream sequence network driven by ordinary differential equations, and our theoretical analysis establishes the convergence guarantee of G²SN. We construct STA-18, the first large-scale benchmark with aligned topological primitive-text pairs across 18 diverse graph datasets. Comprehensive evaluations demonstrate that G²SN achieves state-of-the-art performance on four structural learning tasks (average 3. 2\% F1-score improvement), while our transfer method yields consistent enhancements across 13 downstream tasks (5. 2\% average gains) including 10 large-scale graph datasets. The datasets and code are available at https: //anonymous. 4open. science/r/UGSKT-C10E/.

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NeurIPS Conference 2025 Conference Paper

UniHG: A Large-scale Universal Heterogeneous Graph Dataset and Benchmark for Representation Learning and Cross-Domain Transferring

Yide Qiu
Tong Zhang
Shaoxiang Ling
Xing Cai
Ziqi Gu
Zhen Cui

Irregular data in the real world are usually organized as heterogeneous graphs consisting of multiple types of nodes and edges. However, current heterogeneous graph research confronts three fundamental challenges: i) Benchmark Deficiency, ii) Semantic Disalignment, and iii) Propagation Degradation. In this paper, we construct a large-scale, universal, and joint multi-domain heterogeneous graph dataset named UniHG to facilitate heterogeneous graph representation learning and cross-domain knowledge mining. Overall, UniHG contains 77. 31 million nodes and 564 million directed edges with thousands of labels and attributes, which is currently the largest universal heterogeneous graph dataset available to the best of our knowledge. To perform effective learning and provide comprehensively benchmarks on UniHG, two key measures are taken, including i) the semantic alignment strategy for multi-attribute entities, which projects the feature description of multi-attribute nodes and edges into a common embedding space to facilitate information aggregation; ii) proposing the novel Heterogeneous Graph Decoupling (HGD) framework with a specifically designed Anisotropy Feature Propagation (AFP) module for learning effective multi-hop anisotropic propagation kernels. These two strategies enable efficient information propagation among a tremendous number of multi-attribute entities and meanwhile mine multi-attribute association adaptively through the multi-hop aggregation in large-scale heterogeneous graphs. Comprehensive benchmark results demonstrate that our model significantly outperforms existing methods with an accuracy improvement of 28. 93\%. And the UniHG can facilitate downstream tasks, achieving an NDCG@20 improvement rate of 11. 48\% and 11. 71\%. The UniHG dataset and benchmark codes have been released at https: //anonymous. 4open. science/r/UniHG-AA78.

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ICRA Conference 2022 Conference Paper

DKNAS: A Practical Deep Keypoint Extraction Framework Based on Neural Architecture Search

Li Liu
Xing Cai
Ge Li 0002
Thomas H. Li

Keypoint extraction including both keypoint detection and description is a fundamental step in a wide range of geometric multimedia applications. In recent years, many learning-based approaches for keypoint extraction emerge and achieve promising results. However, they usually design network architectures empirically and lack of considerations about the comprehensive performance, which leads to limited applications. In this paper, we propose a practical framework based on Neural Architecture Search (NAS) technology, DKNAS, which can search architectures automatically and maintain efficiency and effectiveness, simultaneously. To the best of our knowledge, the proposed framework is the first NAS framework for keypoint extraction. The evaluation on HPatches dataset shows that our method achieves state-of-the-art results in the metrics of repeatability, localization error, homography accuracy and matching scores. Besides, our model is applied to a traditional Simultaneous Localization and Mapping (SLAM) system, ORB-SLAM2, to replace the handcrafted keypoints. Experimental results demonstrate that the system adopting our model outperforms ORB-SLAM2 and some other deep keypoints enhanced systems.

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