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Jiaxin Wang

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9 papers

2 author rows

AAAI Conference 2026 Conference Paper

Anchor-Driven Nyström for Deep Graph-Level Clustering

Jiaxin Wang
Wenxuan Tu
Lingren Wang
Jieren Cheng
Yue Yang

Graph-level clustering (GLC), which aims to group entire graphs according to their structural and attribute-based similarities, represents a fundamental yet challenging task in various practical applications. Existing GLC methods primarily fall into two main paradigms: 1) deep graph clustering approaches based on Graph Neural Networks (GNNs), and 2) kernel-based methods that utilize predefined kernels to perform fine-grained structural comparison for clustering. However, GNN-based methods typically learn graph-level representations by aggregating node embeddings through pooling operations, which inevitably leads to substantial information loss and suboptimal clustering performance. In contrast, kernel methods, despite their theoretical expressiveness, suffer from prohibitive computational costs that hinder their scalability to large-scale settings. To solve these issues, we propose a novel graph learning framework named Anchor-driven Nyström for Deep Graph-Level Clustering (ANGC), which computes graph similarity via kernel methods while retaining the scalability of GNNs. Specifically, we first employ GNNs to encode individual graphs into sets of node embeddings. Rather than relying on pooling operations, we compute graph similarities in a kernel space constructed from these embeddings. To enhance both scalability and representational power, we introduce learnable graph Nyström anchors, which support end-to-end optimization and significantly accelerate kernel computations. To further improve the discriminative capability of these anchors, we propose the concept of anchor response discrepancy, that is, the variation in a given anchor’s responses across different samples. By maximizing this discrepancy, the anchors are encouraged to strengthen inter-graph distinctions for better clustering. Extensive experiments demonstrate the effectiveness and superiority of ANGC over existing state-of-the-art methods.

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AAAI Conference 2025 Conference Paper

Federated Graph-Level Clustering Network

Jingxin Liu
Jieren Cheng
Renda Han
Wenxuan Tu
Jiaxin Wang
Xin Peng

Federated graph learning (FGL), which excels in analyzing non-IID graphs as well as protecting data privacy, has recently emerged as a hot topic. Existing FGL methods usually train the client model using labeled data and then collaboratively learn a global model without sharing their local graph data. However, in real-world scenarios, the lack of data annotations impedes the negotiation of multi-source information at the server, leading to sub-optimal feedback to the clients. To address this issue, we propose a novel unsupervised learning framework called Federated Graph-level Clustering Network (FedGCN), which collects the topology-oriented features of non-IID graphs from clients to generate global consensus representations through multi-source clustering structure sharing. Specifically, in the client, we first preserve the prototype features of each cluster from the structure-oriented embedding through clustering and then upload the learned multiple prototypes that are hard to be reconstructed into the raw graph data. In the server, we generate consensus prototypes from multiple condensed structure-oriented signals through Gaussian estimation, which are subsequently transferred to each client to promote the great encoding capacity of the local model for better clustering. Extensive experiments across multiple non-IID graph datasets have demonstrated the effectiveness and superiority of FedGCN against its competitors.

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NeurIPS Conference 2025 Conference Paper

FedIGL: Federated Invariant Graph Learning for Non-IID Graphs

Lingren Wang
Wenxuan Tu
Jiaxin Wang
Xiong Wang
Jieren Cheng
Jingxin Liu

Federated Graph Learning (FGL) effectively facilitates cross-domain graph model training by enabling decentralized learning across multiple domains, while ensuring data privacy through local data storage and communication of model updates instead of raw data. Existing approaches usually assume shared generic knowledge (e. g. , prototypes, spectral features) via aggregating local structures statistically to alleviate structural heterogeneity. However, imposing overly strict assumptions about the presumed correlation between structural features and the global objective often fails in generalizing to local tasks, leading to suboptimal performance. To tackle this issue, we propose a Fed erated I nvariant G raph L earning ( FedIGL ) framework based on invariant learning, which effectively disrupts spurious correlations and further mines the invariant factors across different distributions. Specifically, a server-side global model is trained to capture client-agnostic subgraph patterns shared across clients, whereas client-side models specialize in client-specific subgraph patterns. Subsequently, without compromising privacy, we propose a novel Bi-Gradient Regularization strategy that introduces gradient constraints to guide the model in identifying client-agnostic and client-specific subgraph patterns for better graph representations. Extensive experiments on graph-level clustering and classification tasks demonstrate the superiority of FedIGL against its competitors.

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NeurIPS Conference 2025 Conference Paper

Hierarchical Shortest-Path Graph Kernel Network

Jiaxin Wang
Wenxuan Tu
Jieren Cheng

Graph kernels have emerged as a fundamental and widely adopted technique in graph machine learning. However, most existing graph kernel methods rely on fixed graph similarity estimation that cannot be directly optimized for task-specific objectives, leading to sub-optimal performance. To address this limitation, we propose a kernel-based learning framework called Hierarchical Shortest-Path Graph Kernel Network HSP-GKN, which seamlessly integrates graph similarity estimation with downstream tasks within a unified optimization framework. Specifically, we design a hierarchical shortest-path graph kernel that efficiently preserves both the semantic and structural information of a given graph by transforming it into hierarchical features used for subsequent neural network learning. Building upon this kernel, we develop a novel end-to-end learning framework that matches hierarchical graph features with learnable $hidden$ graph features to produce a similarity vector. This similarity vector subsequently serves as the graph embedding for end-to-end training, enabling the neural network to learn task-specific representations. Extensive experimental results demonstrate the effectiveness and superiority of the designed kernel and its corresponding learning framework compared to current competitors.

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EAAI Journal 2023 Journal Article

Review of resampling techniques for the treatment of imbalanced industrial data classification in equipment condition monitoring

Yage Yuan
Jianan Wei
Haisong Huang
Weidong Jiao
Jiaxin Wang
Hualin Chen

In an actual industrial scenario, machines typically operate normally for the majority of the time, with malfunctions occurring only occasionally. As a result, there is very little recorded data on defects. Consequently, the fault diagnostic dataset becomes imbalanced, with a significantly lower number of fault samples compared to normal samples. Furthermore, with the rapid development of the manufacturing industry, the increasing complexity of machines and equipment leads to various challenges in collecting fault data, such as noise, within-class imbalance, multi-class imbalance, and time series imbalance. It is worth noting that this study is the first to comprehensively summarize these four specific challenges. Therefore, addressing these issues has become a critical research focus and a pain point in the field of fault diagnosis, and numerous solutions have emerged. This study provides a comprehensive overview of these solutions at three levels: data preprocessing, feature extraction, and classifier improvement. It also describes the applications of imbalanced data classification methods, including pure resampling techniques, as well as sampling techniques that combine resampling algorithms with feature extraction and classifier improvement in industrial scenarios. Finally, we summarize the challenges facing imbalanced data classification research and suggest potential directions for future studies.

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IROS Conference 2022 Conference Paper

A Training-Evaluation Method for Nursing Telerobot Operator with Unsupervised Trajectory Segmentation

Jiexin Xie
Deliang Zhu
Jiaxin Wang
Shijie Guo

To cope with the difficulty of training and eval-uation for nursing telerobot operator. This paper proposes a training-evaluation method for operator with unsupervised trajectory segmentation. To evaluate the dexterity and proce-dural knowledge of the operators objectively, we propose a new unsupervised model TSC-CRP that can automatically segment trajectory from nursing robotic training sessions. By comparing the segmented sub-trajectories and the standard sub-trajectory process, the method can provide objective evaluation and meaningful feedback without the intervention from experts. Experiments show that TSC-CRP has higher segmentation accuracy than other unsupervised methods, and it can identify the operators with different skill levels. In practical, the proposed training-evaluation system allows to provide an in-depth analysis of operator action to assess their skills precisely.

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ICML Conference 2021 Conference Paper

Single Pass Entrywise-Transformed Low Rank Approximation

Yifei Jiang
Yi Li 0002
Yiming Sun
Jiaxin Wang
David P. Woodruff

In applications such as natural language processing or computer vision, one is given a large $n \times n$ matrix $A = (a_{i, j})$ and would like to compute a matrix decomposition, e. g. , a low rank approximation, of a function $f(A) = (f(a_{i, j}))$ applied entrywise to $A$. A very important special case is the likelihood function $f\left( A \right ) = \log{\left( \left| a_{ij}\right| +1\right)}$. A natural way to do this would be to simply apply $f$ to each entry of $A$, and then compute the matrix decomposition, but this requires storing all of $A$ as well as multiple passes over its entries. Recent work of Liang et al. shows how to find a rank-$k$ factorization to $f(A)$ using only $n \cdot \poly(\eps^{-1}k\log n)$ words of memory, with overall error $10\|f(A)-[f(A)]_k\|_F^2 + \poly(\epsilon/k) \|f(A)\|_{1, 2}^2$, where $[f(A)]_k$ is the best rank-$k$ approximation to $f(A)$ and $\|f(A)\|_{1, 2}^2$ is the square of the sum of Euclidean lengths of rows of $f(A)$. Their algorithm uses $3$ passes over the entries of $A$. The authors pose the open question of obtaining an algorithm with $n \cdot \poly(\eps^{-1}k\log n)$ words of memory using only a single pass over the entries of $A$. In this paper we resolve this open question, obtaining the first single-pass algorithm for this problem and for the same class of functions $f$ studied by Liang et al. Moreover, our error is $\|f(A)-[f(A)]_k\|_F^2 + \poly(\epsilon/k) \|f(A)\|_F^2$, where $\|f(A)\|_F^2$ is the sum of squares of Euclidean lengths of rows of $f(A)$. Thus our error is significantly smaller, as it removes the factor of $10$ and also $\|f(A)\|_F^2 \leq \|f(A)\|_{1, 2}^2$.

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ICRA Conference 2007 Conference Paper

A Reinforcement Learning Based Dynamic Walking Control

Yong Mao
Jiaxin Wang
Peifa Jia
Shi Li 0002
Zhen Qiu
Le Zhang
Zhuo Han

A quasi-passive dynamic walking robot is built to study natural and energy-efficient biped walking. The robot is actuated by MACCEPA actuators. A reinforcement learning based control method is proposed to enhance the robustness and stability of the robot's walking. The proposed method first learns the desired gait for the robot's walking on a flat floor. Then a fuzzy advantage learning method is used to control it to walk on uneven floor. The effectiveness of the method is verified by simulation results.

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ICRA Conference 1992 Conference Paper

Constraint-based scheduling: a recursive approach

Jiaxin Wang
Pierre Gaspart

The authors introduce the idea of constraint-based scheduling and consider some constraints as a good factor in scheduling. The idea is used to develop a novel scheduling method. A schedule problem is used to present the method. The parallel-serial plan (PSP) is defined. A recursive approach to find the best scheduling for a PSP is outlined. The complexity of the algorithm is discussed, and the algorithm is extended for the scheduling of a plan. >

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